Triple
T7908187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Mueller |
E183627
|
entity |
| Predicate | areaOfExpertise |
P466
|
FINISHED |
| Object |
Google Search ranking systems
Google Search ranking systems are the complex, evolving algorithms and signals Google uses to evaluate and order web pages in its search results.
|
E356366
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Google Search ranking systems | Statement: [John Mueller, areaOfExpertise, Google Search ranking systems]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Search ranking systems Context triple: [John Mueller, areaOfExpertise, Google Search ranking systems]
-
A.
Google Search indexing systems
Google Search indexing systems are the complex set of algorithms and infrastructure Google uses to crawl, process, and organize web content so it can be efficiently retrieved and ranked in search results.
-
B.
PageRank algorithm
The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
-
C.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
-
D.
RankBrain
RankBrain is a machine-learning-based component of Google's search engine that helps interpret and process search queries to deliver more relevant results.
-
E.
AltaVista
AltaVista was one of the earliest and most popular web search engines of the 1990s, known for its fast, comprehensive internet search before being eclipsed by later competitors.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Google Search ranking systems Triple: [John Mueller, areaOfExpertise, Google Search ranking systems]
Generated description
Google Search ranking systems are the complex, evolving algorithms and signals Google uses to evaluate and order web pages in its search results.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Google Search ranking systems Target entity description: Google Search ranking systems are the complex, evolving algorithms and signals Google uses to evaluate and order web pages in its search results.
-
A.
Google Search indexing systems
Google Search indexing systems are the complex set of algorithms and infrastructure Google uses to crawl, process, and organize web content so it can be efficiently retrieved and ranked in search results.
-
B.
PageRank algorithm
The PageRank algorithm is a link analysis method used by search engines, notably Google, to rank web pages in search results based on their importance within the web’s link structure.
-
C.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
"The Anatomy of a Large-Scale Hypertextual Web Search Engine" is a seminal research paper by Sergey Brin and Larry Page that introduced the design and PageRank algorithm behind the early Google search engine.
-
D.
RankBrain
chosen
RankBrain is a machine-learning-based component of Google's search engine that helps interpret and process search queries to deliver more relevant results.
-
E.
AltaVista
AltaVista was one of the earliest and most popular web search engines of the 1990s, known for its fast, comprehensive internet search before being eclipsed by later competitors.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a59de00819099f1ce02bb469e75 |
completed | March 31, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdfe21f9081909d45565867ac7436 |
completed | March 31, 2026, 2:53 p.m. |
| NEDg | Description generation | batch_69cc46bfad4081908de1667b8a10ed0a |
completed | March 31, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc47e7167881908dce54e8b4615900 |
completed | March 31, 2026, 10:17 p.m. |
Created at: March 30, 2026, 5:03 p.m.