Triple
T657072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Clooney |
E11669
|
entity |
| Predicate | activeYearsInCareer |
P18004
|
FINISHED |
| Object | 1978–present |
—
|
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: 1978–present | Statement: [George Clooney, activeYearsInCareer, 1978–present]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInCareer Context triple: [George Clooney, activeYearsInCareer, 1978–present]
-
A.
activeYearsInSport
Indicates the span of years during which an entity actively participated in a particular sport.
-
B.
activeYearsMLB
Indicates the span of years during which an entity was actively participating in Major League Baseball.
-
C.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
D.
yearsActiveAsCoach
Indicates the span of time, typically in years, during which an individual has served in a coaching role.
-
E.
activeYearsEndTime
Indicates the point in time when an entity’s period of activity or operation comes to an end.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4e87408190b5276d2b913d0426 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d121cec81909986c91291bb4ca8 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49ee356c0819085e2e82831cf1360 |
completed | March 1, 2026, 8:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.