Triple
T11299741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hair (1979 film) |
E267551
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object | Don Dacus |
E736372
|
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: Don Dacus | Statement: [Hair (1979 film), starredActor, Don Dacus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Dacus Context triple: [Hair (1979 film), starredActor, Don Dacus]
-
A.
Donnie Dacus
chosen
Donnie Dacus is an American guitarist, singer, and songwriter best known for his tenure with the rock band Chicago in the late 1970s.
-
B.
Tom Dula
Tom Dula was a North Carolina man whose 1868 execution for the murder of Laura Foster inspired the famous American folk ballad "Tom Dooley."
-
C.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
D.
Phil Dusenberry
Phil Dusenberry was an influential American advertising executive and creative director, best known for his groundbreaking work at BBDO and for shaping major campaigns for brands like Pepsi.
-
E.
Mark Dacascos
Mark Dacascos is an American actor and martial artist known for his roles in action films and television, as well as for serving as the Chairman on the TV show "Iron Chef America."
- 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_69d6aac993a08190a6f36445ebaf9a43 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9a3616c8190a8fd23ca67463806 |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f67c5a9dc881909b695f7e87dfcdf6 |
completed | May 2, 2026, 10:36 p.m. |
Created at: April 8, 2026, 9:32 p.m.