Triple
T5461317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Natalia Goncharova |
E122598
|
entity |
| Predicate | hasSpouseGenre |
P44007
|
FINISHED |
| Object | romantic poetry |
—
|
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: romantic poetry | Statement: [Natalia Goncharova, hasSpouseGenre, romantic poetry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpouseGenre Context triple: [Natalia Goncharova, hasSpouseGenre, romantic poetry]
-
A.
genreAssociatedWithSpouse
chosen
Indicates that a particular genre is associated with, or characterizes, the spouse of the referenced entity.
-
B.
hasSpouseInBand
Indicates that a person has a spouse who is a member of the same band.
-
C.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
D.
hasSpouseStyle
Indicates a relationship where one entity’s manner, appearance, or behavior resembles or is characteristic of another entity’s spouse.
-
E.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
- 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a370a88190b5d17b8a5387138d |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.