Triple
T3313728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Ventre de Paris |
E69630
|
entity |
| Predicate | partOfLiteraryProject |
P47219
|
FINISHED |
| Object | study of society under the Second Empire |
—
|
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: study of society under the Second Empire | Statement: [Le Ventre de Paris, partOfLiteraryProject, study of society under the Second Empire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLiteraryProject Context triple: [Le Ventre de Paris, partOfLiteraryProject, study of society under the Second Empire]
-
A.
partOfAuthorProject
Indicates that something belongs to, is included within, or constitutes a component of an author's project.
-
B.
literaryUnit
Indicates that one entity is a distinct segment or component (such as a chapter, scene, or passage) within a larger literary work or text.
-
C.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
D.
literaryCollection
Indicates that one entity is a collection or compilation of literary works that includes or is associated with the other entity.
-
E.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0ef548481908b3aabc7052c70d8 |
completed | March 8, 2026, 5:25 p.m. |
| PD | Predicate disambiguation | batch_69ada4282730819092aa39c5f9269df0 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada52716ec81908e89688a81039394 |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:11 p.m.