Triple
T8552104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Art of Fighting |
E202467
|
entity |
| Predicate | numberOfMainlineGames |
P83577
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Art of Fighting, numberOfMainlineGames, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainlineGames Context triple: [Art of Fighting, numberOfMainlineGames, 3]
-
A.
numberOfKnownPlays
Indicates the total count of plays that are known to be associated with a given entity (such as an author, theater, or period).
-
B.
totalGamesManaged
Indicates the total number of games that an entity has managed in its role (e.g., as a coach or manager).
-
C.
mostGamesWonBy
Indicates that one entity holds the record for having won the greatest number of games compared to others in a given context.
-
D.
gamesPlayed
Indicates the number or set of games that an entity has participated in or completed.
-
E.
numberOfMainEvents
Indicates the total count of primary or most significant events associated with a given entity or context.
- 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_69ca832610e08190b3b6c6cd2c250255 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe886fb788190a73e7c76c4f86409 |
completed | March 31, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69cbd113e05c81908f4f3fc1b5925164 |
completed | March 31, 2026, 1:50 p.m. |
| PDg | Predicate description generation | batch_69cbe30e37ac8190b685df36274602b5 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:19 p.m.