Triple
T38334045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle McCool |
E1037901
|
entity |
| Predicate | isMultipleTimeChampion |
P99897
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Michelle McCool, isMultipleTimeChampion, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMultipleTimeChampion Context triple: [Michelle McCool, isMultipleTimeChampion, yes]
-
A.
hasWonMultipleChampionships
chosen
Indicates that the subject has secured championship titles on more than one separate occasion.
-
B.
numberOfChampionTitles
Indicates the total count of championship titles that an entity has won.
-
C.
isMultipleDivisionChampion
Indicates that an entity has won championship titles in more than one competitive division or category.
-
D.
championSeriesWins
Indicates that one entity has won a championship series a specified number of times or in a specified context relative to another entity.
-
E.
isMultipleOlympicChampion
Indicates that an entity has won Olympic championship titles in more than one edition of the Olympic Games.
- 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_69f76e20d65c81909619ac0dd85c56f0 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcc7a4d7f881908b43b960911b81e9 |
completed | May 7, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69fcc589720c819089c8f500fea3c86a |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:30 p.m.