Triple
T38535575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Williams |
E923487
|
entity |
| Predicate | shotTypeSpecialty |
P30535
|
FINISHED |
| Object | three-point field goals |
—
|
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: three-point field goals | Statement: [Donald Williams, shotTypeSpecialty, three-point field goals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shotTypeSpecialty Context triple: [Donald Williams, shotTypeSpecialty, three-point field goals]
-
A.
shotType
chosen
Indicates the specific kind or category of shot used or taken in a given context (e.g., in film, photography, or sports).
-
B.
shootingStyle
Indicates the characteristic manner or technique with which an entity performs a shooting action (e.g., in sports or photography).
-
C.
sportNumberOfPointsSpecialty
Indicates a relationship where a specific sport or sporting context is associated with a particular number of points tied to a special rule, condition, or specialty within that sport.
-
D.
teamSpecialty
Indicates the particular area of expertise or focus that characterizes a team’s skills or activities.
-
E.
shootoutSpecialist
Indicates a player who is regularly chosen for and excels in taking shots during tie-breaking shootouts.
- 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_69f76ea8f6348190a5c03fb6292bbee3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd2e57c088190a2c5cb0b4a93c145 |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f81cbc8190b4fd3bfc3106c1f3 |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:32 p.m.