Triple
T4256309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Le Mat |
E95981
|
entity |
| Predicate | hasCareerStartAs |
P45002
|
FINISHED |
| Object | film actor |
—
|
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: film actor | Statement: [Paul Le Mat, hasCareerStartAs, film actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareerStartAs Context triple: [Paul Le Mat, hasCareerStartAs, film actor]
-
A.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
B.
hasCareerTrack
Indicates that an entity is associated with or follows a particular career path or professional progression.
-
C.
hasStageCareer
Indicates that an entity has pursued or been involved in a professional career in stage performance or theater.
-
D.
startedActingCareer
chosen
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
E.
hasWorkedIn
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
- 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_69b3453f759881909b91f01a1e82c036 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34ec1971c81908f7a72418efa8bcc |
completed | March 12, 2026, 11:39 p.m. |
| PD | Predicate disambiguation | batch_69b347f73e008190a908a48ef389945a |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:06 p.m.