Triple
T22394622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris barricades of 1832 |
E553596
|
entity |
| Predicate | hasGenreRepresentation |
P120127
|
FINISHED |
| Object | historical novel |
—
|
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: historical novel | Statement: [Paris barricades of 1832, hasGenreRepresentation, historical novel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenreRepresentation Context triple: [Paris barricades of 1832, hasGenreRepresentation, historical novel]
-
A.
hasGenreRelation
Indicates that there is an association between an entity and a specific genre, specifying the type or category it belongs to.
-
B.
hasGenreFeature
chosen
Indicates that something possesses a characteristic, element, or trait associated with a particular genre.
-
C.
hasCanonicalGenre
Indicates that an entity is associated with its primary or officially recognized genre classification.
-
D.
hasGenreAsOutput
Indicates that an entity produces, results in, or outputs a particular genre as its outcome.
-
E.
hasGenreEligibility
Indicates that an entity qualifies to be categorized under a particular genre according to defined criteria.
- 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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1585dac3c8190bc221f35b3eefa3f |
completed | April 29, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69e73015484c8190a9a0b9f554b61a81 |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:45 p.m.