Triple
T26759808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adèle Varens |
E674768
|
entity |
| Predicate | presumedRelationshipToRochester |
P94755
|
FINISHED |
| Object | possible illegitimate daughter |
—
|
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: possible illegitimate daughter | Statement: [Adèle Varens, presumedRelationshipToRochester, possible illegitimate daughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: presumedRelationshipToRochester Context triple: [Adèle Varens, presumedRelationshipToRochester, possible illegitimate daughter]
-
A.
relationshipToBlancheDevereaux
Indicates the specific type of personal or familial relationship an entity has with Blanche Devereaux.
-
B.
relationshipToBaudelaires
Indicates the type of personal or familial connection an entity has to the Baudelaires.
-
C.
relationshipToRelative
chosen
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
-
D.
establishedRelationship
Indicates that a formal, recognized relationship has been created and is now in effect between the referenced entities.
-
E.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
- 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_69eecda6e9dc81908452fab3ba17ed9b |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f618dafb6c8190b4f53a7fcbf967e3 |
completed | May 2, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69f60b8dfa0c8190864e1a940024d0a0 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 27, 2026, 3:57 a.m.