Triple
T17466998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miguel Bosé |
E425301
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Sevilla |
—
|
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: Sevilla | Statement: [Miguel Bosé, notableWork, Sevilla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sevilla Context triple: [Miguel Bosé, notableWork, Sevilla]
-
A.
Sevilla
Sevilla is a station on Madrid's Metro network, serving Line 2 in the city center.
-
B.
Sevilla
Sevilla is a Mexico City Metro station on Line 1, located in the central area of the city and serving nearby commercial and residential zones.
-
C.
Malaga
Malaga is a white wine grape variety name historically used as a synonym for Sémillon in certain wine-growing regions.
-
D.
Seville
chosen
Seville is a historic Spanish city in Andalusia renowned for its rich Moorish and Christian heritage, iconic landmarks like the Giralda and Alcázar, and vibrant cultural traditions such as flamenco.
-
E.
Seville
Seville is a small unincorporated rural community located in Volusia County, Florida, known for its agricultural surroundings and historic character.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451a8f4908190a67a3a82a1c8f011 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.