Triple
T37750205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago Condors |
E940959
|
entity |
| Predicate | hadRosterType |
P16808
|
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: [Chicago Condors, hadRosterType, professional women players]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadRosterType Context triple: [Chicago Condors, hadRosterType, professional women players]
-
A.
hostType
Indicates the category or kind of host associated with or responsible for the related entity or activity.
-
B.
hadasType
Indicates that an entity is classified as being of a particular HADAS type or category.
-
C.
haveType
chosen
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasContactType
Indicates the specific kind or category of contact relationship that exists between two entities.
-
E.
hadStationType
Indicates that an entity (such as a station) possessed or was classified by a particular type or category of station.
- 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_69f76ee1f3a88190834e6c8af99bccc9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaef0cec881908c2742d77d145901 |
completed | May 6, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:19 p.m.