Triple
T28329678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danish Football Player of the Year |
E717504
|
entity |
| Predicate | hasSeparateFemaleAward |
P51490
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Danish Football Player of the Year, hasSeparateFemaleAward, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeparateFemaleAward Context triple: [Danish Football Player of the Year, hasSeparateFemaleAward, yes]
-
A.
awardCategoryGender
Indicates that an award category is designated for recipients of a specific gender.
-
B.
hasSeparateAwardFor
chosen
Indicates that there exists a distinct, dedicated award specifically recognizing the related entity, separate from other general or combined awards.
-
C.
notableFemaleWinner
Indicates that the subject is a female who has achieved a notable or distinguished victory in the specified context.
-
D.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
-
E.
hasFemaleHolders
Indicates that the specified role, position, or item is held or occupied by one or more female individuals.
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69fd64bc86848190a49f451a8fc5cf1e |
completed | May 8, 2026, 4:21 a.m. |
| PD | Predicate disambiguation | batch_69fd5ff4a648819090756d90fd195d9a |
completed | May 8, 2026, 4 a.m. |
Created at: April 28, 2026, 12:31 a.m.