Triple
T8388378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WTA 250 tournaments |
E197877
|
entity |
| Predicate | rankingPointsForRunnerUp |
P26152
|
FINISHED |
| Object | 180 ranking 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: 180 ranking points | Statement: [WTA 250 tournaments, rankingPointsForRunnerUp, 180 ranking points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingPointsForRunnerUp Context triple: [WTA 250 tournaments, rankingPointsForRunnerUp, 180 ranking points]
-
A.
runnerUpScore
Indicates the score achieved by the participant or entity that finished in second place in a competition or ranking.
-
B.
runnerUpRank
Indicates the position or ranking assigned to an entity that finishes immediately after the winner (or near the top) in a competition or ordered list.
-
C.
rankingPoints
chosen
Indicates the number of points assigned to an entity based on its position or performance in a ranking or competition.
-
D.
gamesWonByRunnerUp
Indicates the number of games won by the runner-up in a competition or match.
-
E.
stateOfRunnerUpTeam
Indicates the state or region associated with the team that finished as the runner-up in a competition or event.
- 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_69ca82f749388190bffbea6dfb509016 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb81090f688190a3a8d1680383c361 |
completed | March 31, 2026, 8:08 a.m. |
| PD | Predicate disambiguation | batch_69cb70cfe82881909fe374ba52649e84 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:03 p.m.