Triple
T6453283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dodge City Law |
E139925
|
entity |
| Predicate | playedFormat |
P71089
|
FINISHED |
| Object | indoor American football |
—
|
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: indoor American football | Statement: [Dodge City Law, playedFormat, indoor American football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedFormat Context triple: [Dodge City Law, playedFormat, indoor American football]
-
A.
tourFormat
Indicates the specific structure, style, or type of a tour associated with an entity.
-
B.
competitionFormat
Indicates the specific structure or ruleset under which a competition is organized and conducted.
-
C.
playType
Indicates the specific category or style of play or performance associated with an event or action.
-
D.
trialFormat
Indicates the specific structure, rules, or procedural setup under which a trial or experimental process is conducted.
-
E.
televisionFormat
Indicates the specific type or style of television program or production format associated with an entity.
- 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_69c008b301948190a35854e5284dc822 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069d1c7c481909df9d2369edf5e74 |
completed | March 22, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69c0673b44148190aed70084f0ff4992 |
completed | March 22, 2026, 10:03 p.m. |
| PDg | Predicate description generation | batch_69c068cb3b888190812ed56f2fdd45ed |
completed | March 22, 2026, 10:10 p.m. |
Created at: March 22, 2026, 4:47 p.m.