Triple
T12996594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Treat Williams |
E322049
|
entity |
| Predicate | film |
P9968
|
FINISHED |
| Object | Hair |
E267551
|
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: Hair | Statement: [Treat Williams, film, Hair]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hair Context triple: [Treat Williams, film, Hair]
-
A.
Hair
chosen
Hair is a 1979 musical anti-war film directed by Miloš Forman, adapted from the 1960s stage musical and known for its portrayal of the hippie counterculture and Vietnam War–era America.
-
B.
How To (Hair)
"How To (Hair)" is a track from Esperanza Spalding’s experimental jazz album *12 Little Spells*, which blends innovative composition with conceptual, body-themed song titles.
-
C.
Hair Body Face
"Hair Body Face" is a pop song performed by Lady Gaga from the soundtrack of the 2018 film *A Star Is Born*.
-
D.
The Hair Buyer
The Hair Buyer is a character from the musical "Hamilton," known for purchasing Eliza Hamilton’s hair in the song "Burn."
-
E.
Bangs
Bangs is a surname of English origin borne by various notable individuals, including scientists, writers, and public figures.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7980288190a9fe629a8cc76a52 |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbc4111c8190ace7bf505cd652ee |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:45 p.m.