Triple
T13437834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilhelmina Cooper |
E320275
|
entity |
| Predicate | yearsActiveAsExecutive |
P34676
|
FINISHED |
| Object | 1960s–1980 |
—
|
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: 1960s–1980 | Statement: [Wilhelmina Cooper, yearsActiveAsExecutive, 1960s–1980]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveAsExecutive Context triple: [Wilhelmina Cooper, yearsActiveAsExecutive, 1960s–1980]
-
A.
numberOfExecutives
Indicates the total count of executives associated with a given entity or context.
-
B.
activeInYears
chosen
Indicates that an entity was active or operational during the specified years or year range.
-
C.
yearsActiveAsCoach
Indicates the span of time, typically in years, during which an individual has served in a coaching role.
-
D.
yearsActiveInPolitics
Indicates the span of time during which an entity has been actively involved in political roles, activities, or office.
-
E.
CEOStartYear
Indicates the calendar year in which an individual began serving as CEO of an organization.
- 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.