Triple
T13730273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angélique |
E329776
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Angelique |
E329776
|
NE 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: Angelique | Statement: [Angélique, hasVariant, Angelique]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angelique Context triple: [Angélique, hasVariant, Angelique]
-
A.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
-
B.
Angélique
chosen
Angélique is a French feminine given name historically borne by figures such as Angélique Diderot, the daughter of philosopher Denis Diderot.
-
C.
La Bella
La Bella is a celebrated Renaissance portrait painting by Titian depicting an elegantly dressed young woman, renowned for its rich color and refined beauty.
-
D.
Madama
Madama is a Palestinian village located in the Nablus Governorate in the northern West Bank.
-
E.
Delilah
Delilah is a drama television series that serves as a spin-off of the church-centered family saga Greenleaf, focusing on new characters and legal and personal conflicts.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f92b588190be97ec4564dddd59 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d65062c819086a5f7a7ebc45412 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.