Triple
T17069810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Veronica |
E414184
|
entity |
| Predicate | occupationOfActress |
P125735
|
FINISHED |
| Object | 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: actor | Statement: [Veronica, occupationOfActress, actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationOfActress Context triple: [Veronica, occupationOfActress, actor]
-
A.
occupationInFilm
Indicates that an entity has a specific occupation or role within the context of a particular film.
-
B.
leadActorOccupation
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
C.
starOccupationInSeries
Indicates that an individual has a specific occupation or role as a starring character within a particular series.
-
D.
portrayedByAlsoPlays
Indicates that the actor who portrays a given character also plays another specified role or character.
-
E.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbbfb1f08190807301ff6e573cf5 |
completed | April 18, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69e35d642f74819098c014135e249b27 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:34 a.m.