Triple
T15768878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Harding |
E382296
|
entity |
| Predicate | currentOccupationAtStartOfFilm |
P69186
|
FINISHED |
| Object | television weatherman |
—
|
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: television weatherman | Statement: [Bill Harding, currentOccupationAtStartOfFilm, television weatherman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentOccupationAtStartOfFilm Context triple: [Bill Harding, currentOccupationAtStartOfFilm, television weatherman]
-
A.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
-
B.
hasOccupationDuringStory
chosen
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
C.
earlierOccupation
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
D.
startedActingCareer
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
E.
sonOccupation
Indicates that a specified occupation is the job or professional role held by a person's son.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e051951bac8190a7d45f3612c6de72 |
completed | April 16, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:47 a.m.