Triple
T21811867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manuela Saura Chaplin |
E538493
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Saura |
—
|
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: Saura | Statement: [Manuela Saura Chaplin, familyName, Saura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saura Context triple: [Manuela Saura Chaplin, familyName, Saura]
-
A.
Saura
chosen
Saura is a Spanish surname most prominently associated with acclaimed film director Carlos Saura.
-
B.
Aurai
Aurai are minor wind nymphs in Greek mythology associated with breezes and the lower atmosphere.
-
C.
Anara
Anara is a town in West Bengal, India, known as the birthplace of acclaimed filmmaker and poet Buddhadeb Dasgupta.
-
D.
Sahnish
Sahnish is the self-designation of the Arikara, a Native American people historically associated with the Great Plains region of the United States.
-
E.
Salora
Salora was a prominent Finnish electronics manufacturer best known for producing televisions and radios, and it played a key role in the industrial history of Salo, Finland.
- 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_69e0c473f0f8819086c9d1b4a143bd67 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f07cc6cdf88190a31129acdc3bcec8 |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 16, 2026, 6:53 p.m.