Triple
T10947557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Open Cup 2023 |
E258636
|
entity |
| Predicate | numberOfNISAClubsEntered |
P96781
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [U.S. Open Cup 2023, numberOfNISAClubsEntered, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfNISAClubsEntered Context triple: [U.S. Open Cup 2023, numberOfNISAClubsEntered, 4]
-
A.
numberOfUSASAClubsEntered
Indicates the count of USASA clubs that a given entity has entered or participated in.
-
B.
numberOfPDLClubsEntered
Indicates the count of PDL clubs that an entity has entered or participated in.
-
C.
numberOfNationalSocieties
Indicates the total count of national societies associated with or recognized by a given entity.
-
D.
numberOfMemberOrganizations
Indicates the total count of organizations that are members of a given group, association, or umbrella entity.
-
E.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ebac3c8190849ddda3d9d37327 |
completed | April 9, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69d72e816a98819096d6c10dfb88a66a |
completed | April 9, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69d7322370648190ba14cdd6fb4cdcb0 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:23 p.m.