Triple
T9193579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lezginka |
E220647
|
entity |
| Predicate | femaleStyle |
P78623
|
FINISHED |
| Object | graceful |
—
|
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: graceful | Statement: [Lezginka, femaleStyle, graceful]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleStyle Context triple: [Lezginka, femaleStyle, graceful]
-
A.
fashionStyle
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
B.
femaleFeature
chosen
Indicates that the subject possesses a characteristic or attribute that is typically associated with females.
-
C.
womenSection
Indicates that something is designated as belonging to, located in, or associated with the women's section or area.
-
D.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
E.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
- 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd5c3614c81909e26417e00fdfa11 |
completed | April 1, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69cc660af2408190ae06eb8326e1c64e |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:25 p.m.