Triple
T18571255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Odoyo |
E453876
|
entity |
| Predicate | teamStatusDuringCareer |
P132630
|
FINISHED |
| Object | ICC associate member |
—
|
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: ICC associate member | Statement: [Thomas Odoyo, teamStatusDuringCareer, ICC associate member]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamStatusDuringCareer Context triple: [Thomas Odoyo, teamStatusDuringCareer, ICC associate member]
-
A.
playedCareerStartYear
Indicates the calendar year in which an entity’s playing career (such as a professional or competitive role) began.
-
B.
hasCareerGamesPlayed
Indicates the total number of games an entity has played over the course of its entire career.
-
C.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
playedEntireCareerForSingleFranchise
Indicates that an athlete spent their entire professional career playing for only one franchise or team.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53b021ea4819095fe06be88e74133 |
completed | April 19, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69e478c16e0c8190b03966aa23c395a6 |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e484121cd48190bf583b4c94636a30 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 11:43 a.m.