Triple
T21612137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramal (Ópera–Príncipe Pío) |
E533338
|
entity |
| Predicate | terminusStation |
P15150
|
FINISHED |
| Object | Ópera |
—
|
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: Ópera | Statement: [Ramal (Ópera–Príncipe Pío), terminusStation, Ópera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ópera Context triple: [Ramal (Ópera–Príncipe Pío), terminusStation, Ópera]
-
A.
Ópera
chosen
Ópera is a central Madrid Metro station located near the historic Teatro Real opera house and Plaza de Oriente.
-
B.
Opéra
Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
-
C.
OPERA
OPERA was a long-baseline neutrino oscillation experiment at the Gran Sasso National Laboratory in Italy, designed to detect tau neutrinos in a beam sent from CERN.
-
D.
Opera
Opera is a web browser known for its built-in features like a free VPN, ad blocker, and integrated messaging tools.
-
E.
Opera
Opera is a 1987 Italian horror film directed by Dario Argento, noted for its stylized violence and psychological terror set in the world of grand opera.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3ba79424819094e9ee93c4bbcc0b |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 16, 2026, 6:33 p.m.