Triple
T19040499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Cristina Bezzi-Scali |
E465986
|
entity |
| Predicate | positionHeld |
P8
|
FINISHED |
| Object | Marchesa |
—
|
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: Marchesa | Statement: [Maria Cristina Bezzi-Scali, positionHeld, Marchesa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marchesa Context triple: [Maria Cristina Bezzi-Scali, positionHeld, Marchesa]
-
A.
Marchesa
Marchesa is a luxury fashion label renowned for its ornate, red-carpet-ready eveningwear and bridal gowns.
-
B.
Marchesa
chosen
Marchesa is the Italian noble title traditionally used to designate a woman holding the rank of marquess.
-
C.
Nina Ricci
Nina Ricci is a French luxury fashion house renowned for its elegant haute couture, ready-to-wear collections, and iconic fragrances.
-
D.
Denis of Paris
Denis of Paris is a 3rd-century Christian martyr and bishop, venerated as the patron saint of Paris and traditionally regarded as one of the city’s earliest evangelizers.
-
E.
Kumpire Dior
Kumpire Dior is a high mountain peak in the remote Batura Muztagh subrange of the Karakoram in northern Pakistan.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d80054c88190a9d3a49aed504235 |
completed | April 20, 2026, 7:38 a.m. |
Created at: April 10, 2026, 12:02 p.m.