Triple
T3911047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Metropolitan Gymnasium |
E87321
|
entity |
| Predicate | hasMainArenaCapacity |
P3606
|
FINISHED |
| Object | 10000 |
—
|
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: 10000 | Statement: [Tokyo Metropolitan Gymnasium, hasMainArenaCapacity, 10000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainArenaCapacity Context triple: [Tokyo Metropolitan Gymnasium, hasMainArenaCapacity, 10000]
-
A.
homeArenaCapacity
chosen
Indicates the maximum number of spectators that can be accommodated in an entity’s home arena.
-
B.
homeStadiumCapacity
Indicates the seating capacity of the stadium that serves as a team's or organization's home venue.
-
C.
hasMajorVenue
Indicates that an entity is associated with a primary or principal venue where its main activities or events take place.
-
D.
mainIndoorArena
Indicates that one entity serves as the primary indoor arena or main covered venue associated with another entity.
-
E.
stadiumCapacityApprox
Indicates an approximate number of people that a stadium can accommodate.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:22 p.m.