Triple
T34815198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Moorer vs. George Foreman |
E1003611
|
entity |
| Predicate | ageOfMichaelMoorer |
P181510
|
FINISHED |
| Object | 26 |
—
|
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: 26 | Statement: [Michael Moorer vs. George Foreman, ageOfMichaelMoorer, 26]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageOfMichaelMoorer Context triple: [Michael Moorer vs. George Foreman, ageOfMichaelMoorer, 26]
-
A.
ageInFirstFilm
Indicates the age a person was when they appeared in their first film.
-
B.
Michael B. OlbrichRole
Indicates the specific role, position, or function that Michael B. Olbrich holds or performs in relation to another entity or context.
-
C.
yearsAtHalRoachStudios
Indicates the span of time, measured in years, that an entity was associated with or worked at Hal Roach Studios.
-
D.
ageAt2018MrOlympiaWin
Indicates the age a person was at the time they won the 2018 Mr. Olympia competition.
-
E.
roleOfMichaelGreenberg
Indicates that the specified role or position is held or performed by Michael Greenberg.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ab8abd08190a7f001927cc76a8d |
completed | May 3, 2026, 4:41 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
| PDg | Predicate description generation | batch_69f77a39135081908ae22d2a23b44e74 |
completed | May 3, 2026, 4:39 p.m. |
Created at: May 3, 2026, 3:59 p.m.