Triple
T1746393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagano Olympic Stadium |
E38344
|
entity |
| Predicate | capacityDuring1998WinterOlympics |
P32075
|
FINISHED |
| Object | about 50,000 |
—
|
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: about 50,000 | Statement: [Nagano Olympic Stadium, capacityDuring1998WinterOlympics, about 50,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: capacityDuring1998WinterOlympics Context triple: [Nagano Olympic Stadium, capacityDuring1998WinterOlympics, about 50,000]
-
A.
previousWinterOlympics
Indicates that one Winter Olympic Games event directly preceded another in chronological order.
-
B.
alpineSkiingVenue
Indicates that one entity serves as a venue or location where alpine skiing activities or events take place in relation to another entity.
-
C.
firstWinterGamesCity
Indicates the city where an entity (such as a country, team, or athlete) first participated in the Winter Olympic Games.
-
D.
hostCountryPreviousWinterGamesCity
Indicates that the subject country previously hosted the Winter Games in the city specified as the object.
-
E.
hasIceArena
Indicates that one entity possesses, contains, or includes an ice arena as a facility or feature.
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab630e7d008190a8c673665d9672bb |
completed | March 6, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69aa61c5a18481909bc49e0c54d64314 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab630cb34881908c9fb7ed5dedcd77 |
completed | March 6, 2026, 11:28 p.m. |
Created at: March 4, 2026, 7:31 p.m.