Triple
T1619364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taylor Twellman |
E34794
|
entity |
| Predicate | numberOfMatchesForClub |
P13108
|
FINISHED |
| Object | 174 |
—
|
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: 174 | Statement: [Taylor Twellman, numberOfMatchesForClub, 174]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMatchesForClub Context triple: [Taylor Twellman, numberOfMatchesForClub, 174]
-
A.
homeAndAwayMatchesPerTeam
Indicates that each team plays a specified number of matches both at their home venue and at their opponent’s venue.
-
B.
clubAppearances
chosen
Indicates the number of official matches a player has played for a particular club.
-
C.
associationFootballClub
Indicates that the subject is an association football (soccer) club, i.e., an organized team entity that plays the sport of association football.
-
D.
totalMatchesPerSeason
Indicates the total number of matches associated with an entity within a single season.
-
E.
eligibleClubs
Indicates that certain clubs meet the required criteria or conditions to qualify for a specified status, activity, or benefit.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.