Triple
T31092182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank McLaury |
E792418
|
entity |
| Predicate | roleInGunfightAtOKCorral |
P200606
|
FINISHED |
| Object | armed opponent of the Earp party |
—
|
LITERAL 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: armed opponent of the Earp party | Statement: [Frank McLaury, roleInGunfightAtOKCorral, armed opponent of the Earp party]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInGunfightAtOKCorral Context triple: [Frank McLaury, roleInGunfightAtOKCorral, armed opponent of the Earp party]
-
A.
roleInCreationOfGunsmoke
Indicates that an entity had a specific role or involvement in the creation or production of the work "Gunsmoke."
-
B.
roleAtWoundedKnee
Indicates the specific role or capacity an entity held in relation to the events at Wounded Knee.
-
C.
hasGunfighterProtagonist
Indicates that the main character in the work is a gunfighter, typically skilled in the use of guns and involved in gun-related confrontations.
-
D.
roleInManOnFire
Indicates that an entity has a role or participation in the work titled "Man on Fire."
-
E.
roleInDa5Bloods
Indicates that an entity had an acting role in the film "Da 5 Bloods."
- F. None of above. chosen
Provenance (4 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_69f224cf157c81909e2d2bd88c9282c3 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ff9b1ad27081908f8a492396950795 |
completed | May 9, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69ff9a6354c48190ae21070c1424cb7a |
completed | May 9, 2026, 8:34 p.m. |
| PDg | Predicate description generation | batch_69ff9b19f254819099bb9c034d2b399b |
completed | May 9, 2026, 8:37 p.m. |
Created at: April 29, 2026, 9:02 p.m.