Triple
T12392413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lady with the Glove |
E296027
|
entity |
| Predicate | femaleSubject |
P104712
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [The Lady with the Glove, femaleSubject, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleSubject Context triple: [The Lady with the Glove, femaleSubject, true]
-
A.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
B.
femaleFeature
Indicates that the subject possesses a characteristic or attribute that is typically associated with females.
-
C.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
D.
femaleBranch
Indicates that one entity is a female branch or female-line subdivision of another entity within a hierarchical or genealogical structure.
-
E.
femaleBehavior
Indicates that the behavior or actions being referred to are characteristic of, or typically associated with, females in the given context.
- F. None of above. chosen
Provenance (4 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fd228488190b216abd1c341563c |
completed | April 10, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69d93ed256788190b704cad171a4824e |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d93fa244148190a960be3ff6f1cf45 |
completed | April 10, 2026, 6:21 p.m. |
Created at: April 8, 2026, 9:54 p.m.