Triple
T1168511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NBA Eastern Conference Coach of the Month |
E24854
|
entity |
| Predicate | genderOfRecipients |
P19009
|
FINISHED |
| Object | primarily male |
—
|
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: primarily male | Statement: [NBA Eastern Conference Coach of the Month, genderOfRecipients, primarily male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfRecipients Context triple: [NBA Eastern Conference Coach of the Month, genderOfRecipients, primarily male]
-
A.
hasGenderOfRecipients
chosen
Indicates the gender category or composition of the recipients involved in a given relationship or action.
-
B.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
C.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
D.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
E.
genderedFormOf
Indicates that one term is a gender-specific variant or inflected form corresponding to another, more neutral or differently gendered term.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bccef84481908864e819884af86c |
completed | March 1, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5656948190b0b1d5446ad06005 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.