Triple
T13437833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelmina Cooper |
E320275
|
entity |
| Predicate | yearsActiveAsModel |
P13225
|
FINISHED |
| Object | 1950s–1960s |
—
|
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: 1950s–1960s | Statement: [Wilhelmina Cooper, yearsActiveAsModel, 1950s–1960s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveAsModel Context triple: [Wilhelmina Cooper, yearsActiveAsModel, 1950s–1960s]
-
A.
activeYearsInModeling
chosen
Indicates the span of time during which an entity was actively engaged in modeling.
-
B.
beganModelingCareer
Indicates that an entity started or initiated their professional modeling career at a particular time or under certain circumstances.
-
C.
notableModelingWork
Indicates that an entity is recognized for significant or prominent work in the field of modeling.
-
D.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
E.
liveDebutYear
Indicates the year in which an entity first performed or appeared live (e.g., in concert, on stage, or in a live event).
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaee5ec488190bd0c1e990dbd2bc2 |
completed | April 12, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03926188190ab3948d1f5d3941f |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:40 p.m.