Triple
T33213613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaq Uncut: My Story |
E850226
|
entity |
| Predicate | coWriterOccupation |
P162199
|
FINISHED |
| Object | sports journalist |
—
|
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: sports journalist | Statement: [Shaq Uncut: My Story, coWriterOccupation, sports journalist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coWriterOccupation Context triple: [Shaq Uncut: My Story, coWriterOccupation, sports journalist]
-
A.
coAuthorOccupation
chosen
Indicates that two or more co-authors share the same or closely related professional occupation.
-
B.
authorOccupation
Indicates the professional role or job that an author holds or is associated with.
-
C.
creatorOccupation
Indicates the professional role or job that the creator of an entity holds or held.
-
D.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
E.
occupationAsPersona
Indicates that an entity holds or performs a particular occupation specifically in the role or persona of another characterized identity.
- 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_69f3495fb92c819083ce65d0ddee7a76 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:30 a.m.