Triple
T5746938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cobweb |
E126757
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Paul Stewart |
E239864
|
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: Paul Stewart | Statement: [The Cobweb, starring, Paul Stewart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Stewart Context triple: [The Cobweb, starring, Paul Stewart]
-
A.
Paul Stewart
chosen
Paul Stewart was an American character actor and director known for his tough-guy roles in film noir and classic Hollywood cinema.
-
B.
Tim Stevenson
Tim Stevenson is a British public servant who has served as the ceremonial representative of the monarch in Oxfordshire.
-
C.
Hal Stewart
Hal Stewart is the cameraman-turned-supervillain known as Tighten in the animated film "Megamind."
-
D.
Larry Steers
Larry Steers was a prolific American character actor of the silent and early sound film era, appearing in hundreds of movies in mostly uncredited or supporting roles.
-
E.
Ken Ralston
Ken Ralston is an acclaimed visual effects supervisor known for his groundbreaking work on major films such as the Star Wars and Back to the Future series.
- 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02885b0288190835809681a364b1f |
completed | March 22, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e2c54c0819087ab79ae855b9677 |
completed | March 22, 2026, 11:41 p.m. |
Created at: March 22, 2026, 3:48 p.m.