Triple
T6653664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonia |
E150886
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Antoinetta |
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: Antoinetta | Statement: [Antonia, hasVariant, Antoinetta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antoinetta Context triple: [Antonia, hasVariant, Antoinetta]
-
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.
Giuliana
Giuliana is an Italian feminine given name, commonly considered the female form of Giuliano.
-
D.
Maria
Maria is the birth name of Marie Curie, the pioneering physicist and chemist who conducted groundbreaking research on radioactivity.
-
E.
Maria
Maria is a character in the period drama film "Stage Beauty," which explores gender roles and the world of 17th-century English theatre.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b047eb688190bca86be98ac25e39 |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7007347cc8190a15b4218bb3b7074 |
completed | March 27, 2026, 10:10 p.m. |
Created at: March 27, 2026, 2:01 p.m.