Triple
T13425257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Databricks |
E313462
|
entity |
| Predicate | CEO |
P537
|
FINISHED |
| Object | Ali Ghodsi |
E1038742
|
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: Ali Ghodsi | Statement: [Databricks, CEO, Ali Ghodsi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ali Ghodsi Context triple: [Databricks, CEO, Ali Ghodsi]
-
A.
Ali Ghodsi
chosen
Ali Ghodsi is a computer scientist and entrepreneur best known as the co-founder and CEO of Databricks, a leading data and AI company built around Apache Spark.
-
B.
Arvind Narayanan
Arvind Narayanan is a prominent computer scientist known for his influential work in security, privacy, and the societal impacts of algorithms and machine learning.
-
C.
Parag Agrawal
Parag Agrawal is an Indian-American technology executive and computer scientist best known for serving as the chief executive officer of Twitter.
-
D.
Sanjay Ghemawat
Sanjay Ghemawat is a prominent computer scientist and long-time Google engineer known for co-designing and implementing large-scale distributed systems such as MapReduce, Bigtable, and LevelDB.
-
E.
Anant Agarwal
Anant Agarwal is a computer scientist and MIT professor best known as the founding CEO of edX, a major online learning platform.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaed066408190a416880affd8416e |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7398984f48190adaa1963d261d538 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:40 p.m.