Triple
T20526749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Célimène |
E503955
|
entity |
| Predicate | relationshipTypeWithAlceste |
P140426
|
FINISHED |
| Object | love interest |
—
|
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: love interest | Statement: [Célimène, relationshipTypeWithAlceste, love interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithAlceste Context triple: [Célimène, relationshipTypeWithAlceste, love interest]
-
A.
relationshipToAlcee
Indicates the specific type of relationship or connection that an entity has to Alcee.
-
B.
hasRelationshipTypeWith Valère
Indicates that an entity stands in a specific, characterized type of relationship with Valère.
-
C.
relationshipTypeWithTristan
Indicates the specific nature or category of the relationship that an entity has with Tristan.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
relationshipTypeWithEunice
Indicates the specific nature or category of the relationship that an entity has with Eunice.
- 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a066402081909dd53830eb637ad0 |
completed | April 20, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
| PDg | Predicate description generation | batch_69e5a6a824748190bbe6192d73f3c613 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 16, 2026, 11:37 a.m.