Triple
T25989425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serge Nubret |
E646301
|
entity |
| Predicate | activeYearsInBodybuilding |
P8357
|
FINISHED |
| Object | 1960s–1980s |
—
|
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–1980s | Statement: [Serge Nubret, activeYearsInBodybuilding, 1960s–1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInBodybuilding Context triple: [Serge Nubret, activeYearsInBodybuilding, 1960s–1980s]
-
A.
activeYearsInSport
chosen
Indicates the span of years during which an entity actively participated in a particular sport.
-
B.
ageAt2018MrOlympiaWin
Indicates the age a person was at the time they won the 2018 Mr. Olympia competition.
-
C.
activeYearsPeak
Indicates the span of years during which an entity was at the height of its activity or prominence.
-
D.
activeYearsWith
Indicates the span of time during which an entity was actively engaged in a particular role, activity, or association with another entity.
-
E.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
- 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_69e77e881fc08190ba1c8dc7e2a07f97 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 22, 2026, 8:55 a.m.