Triple
T10253390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Provincial Championship |
E240399
|
entity |
| Predicate | includesMatchType |
P93204
|
FINISHED |
| Object | round-robin matches |
—
|
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: round-robin matches | Statement: [National Provincial Championship, includesMatchType, round-robin matches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesMatchType Context triple: [National Provincial Championship, includesMatchType, round-robin matches]
-
A.
includesMatch
Indicates that one entity contains or encompasses a particular match or matching instance of another entity.
-
B.
matchType
Indicates the specific category or nature of how two or more entities correspond or align with each other within a given context.
-
C.
matchTypes
Indicates that two or more entities share the same type or category according to a specified classification.
-
D.
typicalMatchType
Indicates the usual or most common type of match or pairing that characterizes how two entities are related or aligned.
-
E.
usesMatchesFrom
Indicates that one entity relies on or incorporates matches (e.g., pattern matches, rule matches, or result matches) produced by another entity or process.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d328272c8190a3548d7f7f38cfc4 |
completed | April 7, 2026, 9:49 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ebd6c88190a1f3f4a72a99d6fe |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d32741888190928b045e2241cfac |
completed | April 7, 2026, 9:49 a.m. |
Created at: April 6, 2026, 11:29 a.m.