Triple
T4489342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zev Siegl |
E107329
|
entity |
| Predicate | coFoundedWith |
P2835
|
FINISHED |
| Object | Gordon Bowker |
E167473
|
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: Gordon Bowker | Statement: [Zev Siegl, coFoundedWith, Gordon Bowker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gordon Bowker Context triple: [Zev Siegl, coFoundedWith, Gordon Bowker]
-
A.
Gordon Bowker
chosen
Gordon Bowker is an American writer and entrepreneur best known as one of the co-founders of the global coffee company Starbucks.
-
B.
Gordon Juckes
Gordon Juckes was a prominent Canadian ice hockey administrator who played a key role in developing and promoting amateur hockey across Canada.
-
C.
Martin Boddey
Martin Boddey was a British character actor known for his frequent supporting roles in mid-20th-century films and television, often portraying authority figures such as policemen and officials.
-
D.
Gordon Chambers
Gordon Chambers is an American R&B singer-songwriter and producer best known for penning hits for artists like Anita Baker, Brandy, and Whitney Houston.
-
E.
Gordon Dines
Gordon Dines was a British cinematographer known for his work on mid-20th-century films, particularly comedies and dramas.
- 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_69bd43f84f788190a1383579c4a595be |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd52ad36748190b791de458f2116b2 |
completed | March 20, 2026, 1:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd67a90f308190ab4f912cd1e2f692 |
completed | March 20, 2026, 3:28 p.m. |
Created at: March 20, 2026, 12:59 p.m.