Triple
T3154893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franco Nero |
E65960
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Tristana |
E223574
|
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: Tristana | Statement: [Franco Nero, notableWork, Tristana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tristana Context triple: [Franco Nero, notableWork, Tristana]
-
A.
Tristana
chosen
Tristana is a 1970 Spanish drama film directed by Luis Buñuel, known for its exploration of power, morality, and desire through the story of a young woman and her older guardian.
-
B.
Katarina
Katarina is a feminine given name, commonly used in various European cultures, that is a variant of the name Catherine.
-
C.
Leona
Leona is a feminine given name used in various cultures, often derived from the Latin word for "lion."
-
D.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
E.
Liliana
Liliana is a feminine given name, often considered a more elaborate or romantic variant of Lily, used in various cultures around the world.
- 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_69ad8584485081909ed529e890cadc4a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5e7f4688190b477186254f8a572 |
completed | March 8, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b22503ba208190814ab2bfbe380c42 |
completed | March 12, 2026, 2:29 a.m. |
Created at: March 8, 2026, 3:05 p.m.