Triple
T8342230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Therese |
E195946
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | Thérèse |
E195946
|
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: Thérèse | Statement: [Therese, derivedFrom, Thérèse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thérèse Context triple: [Therese, derivedFrom, Thérèse]
-
A.
Therese
chosen
Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
-
B.
Bénédicte
Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
-
C.
Thérèse Desqueyroux
Thérèse Desqueyroux is a French drama film adaptation of François Mauriac’s novel, centered on a woman trapped in a stifling bourgeois marriage in 1920s provincial France.
-
D.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
E.
Émilie
Émilie is the given first name of the French-born American actress Claudette Colbert, a major Hollywood star of the 1930s and 1940s.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fe9efec81908e0c9ded3963bac5 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc72bc43c81909d95c7eb6aefc403 |
completed | April 2, 2026, 1:32 a.m. |
Created at: March 30, 2026, 5:58 p.m.