Triple
T28993408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kate Sumner |
E736088
|
entity |
| Predicate | hasOccupationAttribute |
P166097
|
FINISHED |
| Object | secret agent |
—
|
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: secret agent | Statement: [Kate Sumner, hasOccupationAttribute, secret agent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationAttribute Context triple: [Kate Sumner, hasOccupationAttribute, secret agent]
-
A.
hasOccupationInferred
Indicates that an entity is inferred, rather than explicitly stated, to have a particular occupation or professional role.
-
B.
hasOccupationInWork
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
-
C.
hasOccupationRelative
Indicates that one entity has another entity as a relative who holds a particular occupation or job.
-
D.
hasOccupationOfDesignee
Indicates that one entity serves as the designated or appointed holder of an occupation or role for another entity.
-
E.
hasTypicalOccupation
Indicates that an entity commonly or characteristically works in a particular job or profession.
- 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_69f077eacd0481908ef0bafd74491cd0 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f65f7f067081909057e9c6e1fd0bdd |
completed | May 2, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69f659d02f1c8190831758ac52bb54e4 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65b136b30819090cf59fb772f35f1 |
completed | May 2, 2026, 8:14 p.m. |
Created at: April 28, 2026, 9:28 a.m.