Triple
T16827659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apel·les Mestres |
E409061
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Teatre líric
Teatre líric is a theatrical work by Catalan writer and illustrator Apel·les Mestres, reflecting his contribution to Catalan literary and stage culture.
|
E1235379
|
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: Teatre líric | Statement: [Apel·les Mestres, notableWork, Teatre líric]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teatre líric Context triple: [Apel·les Mestres, notableWork, Teatre líric]
-
A.
Ópera
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 metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
-
E.
Opera
Opera is a historic Budapest Metro station located beneath Andrássy Avenue, serving the Hungarian State Opera House and the surrounding cultural district.
- 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: Teatre líric Triple: [Apel·les Mestres, notableWork, Teatre líric]
Generated description
Teatre líric is a theatrical work by Catalan writer and illustrator Apel·les Mestres, reflecting his contribution to Catalan literary and stage culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Teatre líric Target entity description: Teatre líric is a theatrical work by Catalan writer and illustrator Apel·les Mestres, reflecting his contribution to Catalan literary and stage culture.
-
A.
Ópera
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 metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
-
E.
Opera
Opera is a historic Budapest Metro station located beneath Andrássy Avenue, serving the Hungarian State Opera House and the surrounding cultural district.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3151350819097b1c375e6df8986 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b2a0ac148190a7a7edebcb67c040 |
completed | May 10, 2026, 4:30 p.m. |
| NEDg | Description generation | batch_6a00b35ea8f88190ae33e8a2f906d133 |
completed | May 10, 2026, 4:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00b3d14b3c819081f435777f47eca3 |
completed | May 10, 2026, 4:35 p.m. |
Created at: April 10, 2026, 5:23 a.m.