Triple
T15234827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yellow Line (Budapest Metro) |
E364096
|
entity |
| Predicate | notableStation |
P3858
|
FINISHED |
| Object |
Opera
Opera is a historic Budapest Metro station located beneath Andrássy Avenue, serving the Hungarian State Opera House and the surrounding cultural district.
|
E1144348
|
NE FINISHED |
How this triple was built (4 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: Opera | Statement: [Yellow Line (Budapest Metro), notableStation, Opera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opera Context triple: [Yellow Line (Budapest Metro), notableStation, Opera]
-
A.
Opera
Opera is a web browser known for its built-in features like a free VPN, ad blocker, and integrated messaging tools.
-
B.
Opera
Opera is a metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
-
C.
Ópera
Ópera is a central Madrid Metro station located near the historic Teatro Real opera house and Plaza de Oriente.
-
D.
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.
-
E.
Opéra
Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Opera Triple: [Yellow Line (Budapest Metro), notableStation, Opera]
Generated description
Opera is a historic Budapest Metro station located beneath Andrássy Avenue, serving the Hungarian State Opera House and the surrounding cultural district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Opera Target entity description: Opera is a historic Budapest Metro station located beneath Andrássy Avenue, serving the Hungarian State Opera House and the surrounding cultural district.
-
A.
Opera
Opera is a web browser known for its built-in features like a free VPN, ad blocker, and integrated messaging tools.
-
B.
Opera
Opera is a metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
-
C.
Ópera
Ópera is a central Madrid Metro station located near the historic Teatro Real opera house and Plaza de Oriente.
-
D.
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.
-
E.
Opéra
Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
- F. None of above. chosen
Provenance (5 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007d91e4881908ea52d11a3d4480a |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd3dd5a081909a1a7fceda648c29 |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fede8eed2c8190adb45306a1ec0faa |
completed | May 9, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fedef546708190bdeedd2c61fdbc86 |
completed | May 9, 2026, 7:15 a.m. |
Created at: April 10, 2026, 3:12 a.m.