Triple
T21047728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midtstubakken |
E518491
|
entity |
| Predicate | hillRecordHolderGender |
P79777
|
FINISHED |
| Object | men and women |
—
|
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: men and women | Statement: [Midtstubakken, hillRecordHolderGender, men and women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hillRecordHolderGender Context triple: [Midtstubakken, hillRecordHolderGender, men and women]
-
A.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
-
B.
recordHolderMen
chosen
Indicates that the subject is the male athlete who holds the record for a specified event or category.
-
C.
genderOfEponym
Indicates the gender of the person after whom something (such as a place, object, or concept) is named.
-
D.
genderOfFirstHolder
Indicates that the relationship specifies the gender of the first entity that holds or possesses something in the described context.
-
E.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf5b01481909db49aa5be3846aa |
completed | April 21, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:34 p.m.