Triple
T21987716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darin Erstad |
E543007
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Darin |
—
|
NE NERFINISHED |
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: Darin | Statement: [Darin Erstad, givenName, Darin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darin Context triple: [Darin Erstad, givenName, Darin]
-
A.
Darin
chosen
Darin is a given name, typically a masculine first name, used as a variant spelling of Darren.
-
B.
Darin McFadyen
Darin McFadyen is a New Zealand-born DJ and producer best known for his breakbeat and electronic music work under the stage name Freq Nasty.
-
C.
Tony Darrow
Tony Darrow is an American actor best known for his supporting roles as mobsters in films and television, particularly in Martin Scorsese’s crime dramas.
-
D.
Eric Hatch
Eric Hatch was an American author and screenwriter best known for his witty stories and adaptations in 1930s Hollywood comedies.
-
E.
Don James
Don James was a highly successful American college football coach best known for leading the University of Washington Huskies to national prominence, including a share of the 1991 national championship.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c48136b081908831fa907cc02e18 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1270ad0d48190bd822289ce18195c |
completed | April 28, 2026, 9:30 p.m. |
Created at: April 16, 2026, 8:04 p.m.