Triple
T16960952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coloso de José Díaz |
E411424
|
entity |
| Predicate | hasCapacityCategory |
P96445
|
FINISHED |
| Object | large-capacity stadium |
—
|
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: large-capacity stadium | Statement: [Coloso de José Díaz, hasCapacityCategory, large-capacity stadium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCapacityCategory Context triple: [Coloso de José Díaz, hasCapacityCategory, large-capacity stadium]
-
A.
hasCapacityType
Indicates that an entity possesses a specific kind or classification of capacity or capability.
-
B.
hasCapacityTo
Indicates that one entity possesses the ability, power, or potential to perform an action or bring about a particular effect in relation to another entity or context.
-
C.
hasCapacityProperty
Indicates that an entity is associated with a capacity-related characteristic, such as volume, throughput, or maximum amount it can hold or handle.
-
D.
hasCapType
Indicates that an entity possesses or is characterized by a specific type of cap or cap-like feature.
-
E.
hasSeatingCapacityCategory
chosen
Indicates the classification of an entity based on the range or category of how many people it can seat.
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0209a9081909d9c62456bc16e14 |
completed | April 18, 2026, 6:40 p.m. |
| PD | Predicate disambiguation | batch_69e32b9cddf88190bc42709604047353 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:31 a.m.