Triple
T15635980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Emma, Lady Hamilton as a Bacchante |
E375945
|
entity |
| Predicate | hasFemaleSubject |
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: [Portrait of Emma, Lady Hamilton as a Bacchante, hasFemaleSubject, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleSubject Context triple: [Portrait of Emma, Lady Hamilton as a Bacchante, hasFemaleSubject, true]
-
A.
hasFemaleSpeaker
Indicates that the associated content, event, or communication is spoken or narrated by a female individual.
-
B.
femaleSubject
chosen
Indicates that the subject in the relationship or action is female.
-
C.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
D.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
E.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb8b4c48190b80fea6877483089 |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda868d4481908f4bce1c64d2902a |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:14 a.m.