Triple
T29588642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romani of Paris |
E754093
|
entity |
| Predicate | historicallyDepictedIn |
P126620
|
FINISHED |
| Object | French literature |
—
|
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: French literature | Statement: [Romani of Paris, historicallyDepictedIn, French literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicallyDepictedIn Context triple: [Romani of Paris, historicallyDepictedIn, French literature]
-
A.
notableDepictionBy
Indicates that an entity is significantly portrayed or represented by a particular creator, work, or medium.
-
B.
historicallyRepresentedIn
chosen
Indicates that something has been depicted, documented, or portrayed within a historical context, source, or period.
-
C.
involvesHistoricalFigures
Indicates that the subject is associated with or includes participation by notable individuals from history.
-
D.
isPortrayedIn
Indicates that an entity is depicted or represented within a particular work, medium, or portrayal.
-
E.
hasYoungPortrayalOf
Indicates that one entity is a portrayal or depiction of another entity specifically in their younger age or earlier life stage.
- 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_69f0ef836ac88190bd809dc58b5ec907 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69fe8ddf70e48190a917eb9e8f7b6966 |
completed | May 9, 2026, 1:29 a.m. |
| PD | Predicate disambiguation | batch_69fe87ef94dc81909bb00ec8d6de9bcd |
completed | May 9, 2026, 1:03 a.m. |
Created at: April 28, 2026, 6:12 p.m.