Triple
T10290240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Double X |
E241342
|
entity |
| Predicate | hasCareerGamesPlayed |
P93278
|
FINISHED |
| Object | 2317 |
—
|
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: 2317 | Statement: [Double X, hasCareerGamesPlayed, 2317]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareerGamesPlayed Context triple: [Double X, hasCareerGamesPlayed, 2317]
-
A.
careerGamesStarted
Indicates the total number of games an entity has started over the course of its entire career.
-
B.
hasCareerSpanCoverage
Indicates that one entity’s coverage, record, or data extends across the full duration of another entity’s career span.
-
C.
playedCareerStartYear
Indicates the calendar year in which an entity’s playing career (such as a professional or competitive role) began.
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
hasPlayedProfessionalSports
Indicates that an entity has participated as an athlete in an officially recognized professional-level sports competition or league.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f117708190928f92ae2611d724 |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d7cada7881908beba55a1dc9ecb9 |
completed | April 7, 2026, 10:09 a.m. |
Created at: April 6, 2026, 11:41 a.m.