Triple
T3672851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stripes |
E77918
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Bill Butler |
E33313
|
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: Bill Butler | Statement: [Stripes, cinematographyBy, Bill Butler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Butler Context triple: [Stripes, cinematographyBy, Bill Butler]
-
A.
Bill Butler
chosen
Bill Butler was an acclaimed American cinematographer best known for his influential work on landmark films of the 1970s and 1980s.
-
B.
Reg Butler
Reg Butler was a British sculptor known for his expressive, often skeletal metal figures and his prominence in postwar European modernist sculpture.
-
C.
Max Cullen
Max Cullen is an Australian character actor known for his extensive work in film, television, and theatre over several decades.
-
D.
Dan Hughes
Dan Hughes is an American basketball coach best known for leading the WNBA’s Seattle Storm to a championship and for his long, successful career coaching multiple WNBA franchises.
-
E.
Don Beyer
Don Beyer is an American Democratic politician and former Lieutenant Governor of Virginia who serves in the U.S. House of Representatives.
- 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_69ad85e083008190b2e1b7085fe500bd |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc42f82548190b4d5f0fe7250decb |
completed | March 8, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b488526214819092abb95f7cf119d5 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:25 p.m.