Triple
T5532642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francesca Eastwood |
E145084
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Francesca |
E83896
|
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: Francesca | Statement: [Francesca Eastwood, givenName, Francesca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francesca Context triple: [Francesca Eastwood, givenName, Francesca]
-
A.
Francesca
chosen
Francesca is an Italian given name, traditionally the feminine form of Francesco and commonly used in Italian-speaking and other European cultures.
-
B.
Francesca da Rimini
Francesca da Rimini is a tragic noblewoman from Dante Alighieri’s Divine Comedy, renowned for her doomed love affair with Paolo Malatesta and her poignant appearance among the lustful in the Inferno.
-
C.
Leonora
Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
-
D.
Leonora
Leonora is a remote mining town in Western Australia’s Goldfields-Esperance region, historically significant for its goldfields and outback heritage.
-
E.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
- 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_69c008f9955881909bfa8348b56b4739 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f9ea2c88190a68642f5799bd8ff |
completed | March 22, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c028094fa48190a1f48779a7963af9 |
completed | March 22, 2026, 5:34 p.m. |
Created at: March 22, 2026, 3:34 p.m.