Triple
T22221450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Ewry |
E549218
|
entity |
| Predicate | totalOlympicGoldMedalsIncludingIntercalated |
P35180
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Ray Ewry, totalOlympicGoldMedalsIncludingIntercalated, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalOlympicGoldMedalsIncludingIntercalated Context triple: [Ray Ewry, totalOlympicGoldMedalsIncludingIntercalated, 10]
-
A.
totalOlympicGoldMedals
chosen
Indicates the total number of Olympic gold medals that an entity has won.
-
B.
totalOlympicMedals
Indicates the total number of Olympic medals an entity has earned across all Games and events.
-
C.
olympicGoldMedals
Indicates that an entity has won one or more Olympic gold medals.
-
D.
totalOlympicBronzeMedals
Indicates the total number of Olympic bronze medals that an entity has earned or been awarded.
-
E.
olympicGoldWith
Indicates that the related entities won an Olympic gold medal together, typically as teammates in the same event or competition.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b90b1dc81908fee5fab8d5d14a2 |
completed | April 28, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 8:37 p.m.