Triple
T14371426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hummingbird |
E356365
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Google RankBrain |
E356366
|
NE FINISHED |
How this triple was built (2 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 RankBrain | Statement: [Hummingbird, relatedTo, Google RankBrain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google RankBrain Context triple: [Hummingbird, relatedTo, Google RankBrain]
-
A.
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.
-
B.
Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
-
C.
Google Tensor
Google Tensor is Google's custom-designed system-on-a-chip (SoC) platform created to power Pixel devices with advanced AI and machine learning capabilities.
-
D.
Google Knowledge Graph
Google Knowledge Graph is a large-scale semantic database that organizes information about entities and their relationships to enhance Google’s search and contextual understanding capabilities.
-
E.
IBM Watson
IBM Watson is IBM’s artificial intelligence platform known for its natural language processing, machine learning capabilities, and high-profile applications such as winning on Jeopardy! and powering enterprise AI solutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb2082c8190b42cc5f2bab4f574 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c5363a081909681b54c1d8218dc |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:15 a.m.