Triple
T2250003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antoinette Dunant |
E49594
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Antoinette |
E182168
|
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: Antoinette | Statement: [Antoinette Dunant, hasGivenName, Antoinette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antoinette Context triple: [Antoinette Dunant, hasGivenName, Antoinette]
-
A.
Antoinette
chosen
Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
-
B.
Antoinette
Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
C.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
D.
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.
-
E.
Laetitia
Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc11b61888190af3b11b87dc8e0dc |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae96021324819088eafcaf06f97455 |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:47 p.m.