Triple
T31151500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biathlon at the 2014 Winter Olympics |
E794086
|
entity |
| Predicate | featuresGender |
P46670
|
FINISHED |
| Object | men |
—
|
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 | Statement: [Biathlon at the 2014 Winter Olympics, featuresGender, men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresGender Context triple: [Biathlon at the 2014 Winter Olympics, featuresGender, men]
-
A.
featuredGender
chosen
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
B.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
C.
bearerGender
Indicates the gender associated with the bearer in the relationship or context.
-
D.
genderCategoryIncludes
Indicates that a given gender category encompasses or contains the specified gender identity or subgroup.
-
E.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
- 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_69f224d41bb48190a5621cd1485e3a30 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0056174c908190be99c91a70393e47 |
completed | May 10, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_6a00538e7e08819091ecd4316cd641a1 |
completed | May 10, 2026, 9:44 a.m. |
Created at: April 29, 2026, 9:06 p.m.