Triple
T12943847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Golden Fang |
E309708
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Coy Harlingen |
E309706
|
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: Coy Harlingen | Statement: [Golden Fang, associatedWith, Coy Harlingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coy Harlingen Context triple: [Golden Fang, associatedWith, Coy Harlingen]
-
A.
Coy Harlingen
chosen
Coy Harlingen is a fictional saxophonist and presumed-dead ex-junkie turned government informant in Thomas Pynchon's novel "Inherent Vice."
-
B.
Jesus Tarango
Jesus Tarango is a Native American tribal leader who serves as the chairperson of the Wilton Rancheria in California.
-
C.
Eloy Garza
Eloy Garza is an individual notable enough to be recognized as a distinguished bearer of the surname Garza.
-
D.
Tony Garza
Tony Garza is an American attorney and former U.S. Ambassador to Mexico known for his work in diplomacy and U.S.–Mexico relations.
-
E.
Rey Gallegos
Rey Gallegos is an American actor known for his work in film and television, including a notable role in the HBO miniseries "Generation Kill."
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1a28688190ab9fd1307bc76b4a |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af73e6348190be114e8c5ad181bf |
completed | May 3, 2026, 2:14 a.m. |
Created at: April 9, 2026, 5:43 p.m.