Triple
T36935666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Professional Soccer League (indoor) |
E913607
|
entity |
| Predicate | hadBoardedField |
P186707
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [National Professional Soccer League (indoor), hadBoardedField, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadBoardedField Context triple: [National Professional Soccer League (indoor), hadBoardedField, true]
-
A.
hasBoardingType
Indicates the specific manner or method by which an entity is boarded or accessed (e.g., how passengers or items are taken on).
-
B.
hasBoardingOption
Indicates that an entity offers or is associated with a particular way or method by which passengers or items can board or be taken on.
-
C.
hadBoardingSystem
Indicates that an entity used or was equipped with a particular boarding system for embarking or disembarking passengers or cargo.
-
D.
isBoarding
Indicates that an entity is in the process of getting onto or entering a vehicle, vessel, or similar mode of transport.
-
E.
hasBoardingAreaFor
Indicates that one entity provides or contains a designated area where passengers can board another entity (such as a vehicle or vessel).
- 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_69f76e896c988190880c130e01303dd4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf79ddb08190a083405cccc14137 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.