Triple
T806297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnold Schwarzenegger |
E17440
|
entity |
| Predicate | numberOfMrOlympiaTitles |
P21252
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Arnold Schwarzenegger, numberOfMrOlympiaTitles, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMrOlympiaTitles Context triple: [Arnold Schwarzenegger, numberOfMrOlympiaTitles, 7]
-
A.
worldChampionshipTitles
Indicates the number of world championship titles an entity has won.
-
B.
mastersTitles
Indicates that one entity holds one or more master's degree titles associated with another entity (such as an institution, field, or program).
-
C.
WorldChampionshipAppearances
Indicates the number of times an entity has participated in a world championship competition.
-
D.
worldChampionshipGoldMedals
Indicates the number of gold medals an entity has won at world championship competitions.
-
E.
majorChampionshipWins
Indicates the number of major championship titles an entity has won.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ac07fedc8190ab05595f25c1792f |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7221c081908068e66fe720f26d |
completed | March 1, 2026, 9:06 p.m. |
| PDg | Predicate description generation | batch_69a4ac0688708190b62ac0a8239ec8c8 |
completed | March 1, 2026, 9:13 p.m. |
Created at: March 1, 2026, 7:38 p.m.