Triple
T23044402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hippolyte Jean Blanc |
E573836
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Blanc |
—
|
NE NERFINISHED |
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: Blanc | Statement: [Hippolyte Jean Blanc, familyName, Blanc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blanc Context triple: [Hippolyte Jean Blanc, familyName, Blanc]
-
A.
Blanc
chosen
Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
-
B.
Bianco
Bianco is an Italian surname commonly associated with individuals of Italian heritage, including the artist Enrico Bianco.
-
C.
Branco
Branco is a Portuguese surname borne by various notable figures, including architects, artists, and public personalities.
-
D.
Weiß
Weiß is a German surname commonly borne by individuals of German-speaking origin.
-
E.
Бяла
Бяла is a small Bulgarian town known for its location near the Black Sea coast and its mix of historical and seaside tourism attractions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1851811e08190a5af6a112687327e |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:54 p.m.