Triple
T22146980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nightingale of India |
E547310
|
entity |
| Predicate | appliedToGender |
P2452
|
FINISHED |
| Object | female singer |
—
|
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: female singer | Statement: [Nightingale of India, appliedToGender, female singer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToGender Context triple: [Nightingale of India, appliedToGender, female singer]
-
A.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
sponsoredGender
Indicates that one entity provides financial or material sponsorship specifically related to the gender of another entity.
-
E.
hasGenderFocus
chosen
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
- 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_69e11e3a95d88190a3bd80d9471976c3 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129f156988190bc9a24a37418e849 |
completed | April 28, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69e71b384e008190b723c9a0f1089d66 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:33 p.m.