Triple
T1384665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kansas City Current |
E29816
|
entity |
| Predicate | hasRosterType |
P121
|
FINISHED |
| Object | professional women players |
—
|
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: professional women players | Statement: [Kansas City Current, hasRosterType, professional women players]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRosterType Context triple: [Kansas City Current, hasRosterType, professional women players]
-
A.
hasMemberType
chosen
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
B.
hasContactType
Indicates the specific kind or category of contact relationship that exists between two entities.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasServiceType
Indicates that an entity is associated with or categorized by a particular type of service.
-
E.
hasRecordType
Indicates that an entity is associated with or classified under a specific type or category of record.
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c33896548190b44f70c9aaaed9b6 |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.