Triple
T4845694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ABA Rookie of the Year Award (Mel Daniels, 1967–68) |
E108283
|
entity |
| Predicate | relatedAwardType |
P1619
|
FINISHED |
| Object | rookie of the year in professional basketball |
—
|
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: rookie of the year in professional basketball | Statement: [ABA Rookie of the Year Award (Mel Daniels, 1967–68), relatedAwardType, rookie of the year in professional basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedAwardType Context triple: [ABA Rookie of the Year Award (Mel Daniels, 1967–68), relatedAwardType, rookie of the year in professional basketball]
-
A.
relatedAward
Indicates that there is an award connected or associated with the subject entity, such as an honor, prize, or recognition related to it.
-
B.
typicalAwardType
Indicates the usual or most common type or category of award associated with a given entity or context.
-
C.
awardType
chosen
Indicates the specific category or kind of award associated with an entity or event.
-
D.
awardTo
Indicates that an award, prize, or honor is given or assigned to a particular recipient.
-
E.
recipientOfAward
Indicates that an entity has received or been granted a particular award.
- 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_69bd4409b264819085ab855f3eb5381a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e01872c81909607010c10538ad1 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2375a4819098e16acb982c8fab |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:25 p.m.