Triple
T15632285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trees Lounge |
E375844
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Chris Hanley |
E387837
|
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: Chris Hanley | Statement: [Trees Lounge, producer, Chris Hanley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Hanley Context triple: [Trees Lounge, producer, Chris Hanley]
-
A.
Chris Hanley
chosen
Chris Hanley is an American film producer known for backing distinctive independent films such as "Buffalo ’66" and "American Psycho."
-
B.
Robert Charles Hunter
Robert Charles Hunter is known primarily as the former husband of American actress Diane Ladd.
-
C.
James Coryell
James Coryell was a Texas frontiersman and early settler whose legacy is commemorated by having Coryell County, Texas, named in his honor.
-
D.
Daniel Kottke
Daniel Kottke is an early Apple employee and close college friend of Steve Jobs who worked on the original Apple computers.
-
E.
Mike Seeger
Mike Seeger was an influential American folk musician, folklorist, and collector who played a key role in the mid-20th-century revival of traditional old-time music.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb7338881909f3c430bb73f91d1 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f472b648190b7cd532a1b16373e |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.