Triple
T19090630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tricia Helfer |
E467270
|
entity |
| Predicate | yearOfCareerStartAsActress |
P45002
|
FINISHED |
| Object | early 2000s |
—
|
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: early 2000s | Statement: [Tricia Helfer, yearOfCareerStartAsActress, early 2000s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearOfCareerStartAsActress Context triple: [Tricia Helfer, yearOfCareerStartAsActress, early 2000s]
-
A.
debutAsLeadActressYear
Indicates the year in which an entity first made her debut as a lead actress.
-
B.
startedActingCareer
chosen
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
C.
startedCareerAsChildActor
Indicates that a person began their professional career in acting during childhood.
-
D.
beganModelingCareer
Indicates that an entity started or initiated their professional modeling career at a particular time or under certain circumstances.
-
E.
ageInFirstFilm
Indicates the age a person was when they appeared in their first film.
- 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e34b6b348190bb868356ed8b655a |
completed | April 20, 2026, 8:26 a.m. |
| PD | Predicate disambiguation | batch_69e4b9a604308190a3235184f9f2c056 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:04 p.m.