Triple
T21406695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier des Grieux |
E528052
|
entity |
| Predicate | relationshipTypeWithManonLescaut |
P143847
|
FINISHED |
| Object | tragic lover |
—
|
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: tragic lover | Statement: [Chevalier des Grieux, relationshipTypeWithManonLescaut, tragic lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithManonLescaut Context triple: [Chevalier des Grieux, relationshipTypeWithManonLescaut, tragic lover]
-
A.
relationshipToEdmondDantès
Indicates the specific type of personal or social relationship an entity has with Edmond Dantès.
-
B.
relationshipWithHumbertHumbert
Indicates that an entity has a specified type of personal, emotional, or social relationship with Humbert Humbert.
-
C.
relationshipToSaint-Preux
Indicates a personal or social connection that one entity has to the figure Saint-Preux.
-
D.
relationshipToThérèse Raquin
Indicates the specific type of relationship or connection an entity has to Thérèse Raquin.
-
E.
relationshipToHesterPrynne
Indicates the specific familial, social, or emotional connection that an entity has to Hester Prynne.
- 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b1b08fdc81909b3ba01add5f6484 |
completed | April 22, 2026, 11:32 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 5:32 p.m.