Triple
T24194127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premier League 2004–05 |
E599781
|
entity |
| Predicate | titleWinningTeamPoints |
P26207
|
FINISHED |
| Object | Chelsea F.C. 95 points |
—
|
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: Chelsea F.C. 95 points | Statement: [Premier League 2004–05, titleWinningTeamPoints, Chelsea F.C. 95 points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleWinningTeamPoints Context triple: [Premier League 2004–05, titleWinningTeamPoints, Chelsea F.C. 95 points]
-
A.
winningTeamScore
chosen
Indicates the number of points or goals achieved by the team that wins a particular game or competition.
-
B.
winningTeam
Indicates which team is the victor in a given competition, game, or contest.
-
C.
game4WinningTeam
Indicates which team won the specified game.
-
D.
managerWinningTeam
Indicates that the subject is the manager or coach of the team that won a particular competition or match.
-
E.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
- F. None of above.
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_69e288cdc8b88190bf2f835d3cb4ca28 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e24947948190b609a39a2b8828ad |
completed | April 29, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:36 p.m.