Triple
T21726952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wasim Akram |
E536297
|
entity |
| Predicate | odiCareerSpan |
P18004
|
FINISHED |
| Object | 1984–2003 |
—
|
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: 1984–2003 | Statement: [Wasim Akram, odiCareerSpan, 1984–2003]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: odiCareerSpan Context triple: [Wasim Akram, odiCareerSpan, 1984–2003]
-
A.
careerSpanTeam
Indicates the team for which an individual’s entire career span (or a defined portion of it) is being measured or associated.
-
B.
winnerCareerSpanTeam
Indicates the team for which a winner competed during the span of their professional 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
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
hasCareerSpanCoverage
Indicates that one entity’s coverage, record, or data extends across the full duration of another entity’s career span.
- 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_69e0c46d3284819099a4f9d5a704eb95 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd973ac648190bb09e20ac1be2d9b |
completed | April 27, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e6969725bc81908e7ad19619ba2688 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:48 p.m.