Triple
T1172013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States women's national soccer team |
E24933
|
entity |
| Predicate | wonOlympicGold |
P6616
|
FINISHED |
| Object | 1996 Summer Olympics women's football tournament |
—
|
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: 1996 Summer Olympics women's football tournament | Statement: [United States women's national soccer team, wonOlympicGold, 1996 Summer Olympics women's football tournament]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wonOlympicGold Context triple: [United States women's national soccer team, wonOlympicGold, 1996 Summer Olympics women's football tournament]
-
A.
wonOlympicGoldYear
Indicates that an entity won an Olympic gold medal in the specified year.
-
B.
olympicGoldMedals
chosen
Indicates that an entity has won one or more Olympic gold medals.
-
C.
OlympicMedal
Indicates that an entity has been awarded an Olympic medal in a specific event or discipline.
-
D.
OlympicGoldMedalSport
Indicates that the subject sport is one in which the object athlete or team has won an Olympic gold medal.
-
E.
worldChampionshipGoldMedals
Indicates the number of gold medals an entity has won at world championship competitions.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bceb3f188190b8b767380fe5986f |
completed | March 1, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5656948190b0b1d5446ad06005 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.