Triple
T2015453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dominique Gisin |
E43784
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Dominique |
E134253
|
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: Dominique | Statement: [Dominique Gisin, givenName, Dominique]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dominique Context triple: [Dominique Gisin, givenName, Dominique]
-
A.
Dominique
chosen
Dominique is a French given name commonly used for both males and females, notably borne by figures such as former IMF chief Dominique Strauss-Kahn.
-
B.
Françoise
Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
-
C.
Sophie Dumond
Sophie Dumond is Arthur Fleck’s single-mother neighbor and tentative love interest in the 2019 film "Joker," representing his yearning for connection and normalcy amid his psychological unraveling.
-
D.
Clémentine
Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
-
E.
Madeleine
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
- 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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8cb16048190bc626685fbb5f707 |
completed | March 7, 2026, 5:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d85c2208190bbd612a3ecbbacfd |
completed | March 9, 2026, 5:41 a.m. |
Created at: March 4, 2026, 7:37 p.m.