Triple
T8455125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marignane |
E199900
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Figueres, Spain |
E42935
|
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: Figueres, Spain | Statement: [Marignane, hasTwinTown, Figueres, Spain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Figueres, Spain Context triple: [Marignane, hasTwinTown, Figueres, Spain]
-
A.
Figueres
chosen
Figueres is a town in Catalonia, Spain, best known as the birthplace of surrealist artist Salvador Dalí and home to the Dalí Theatre-Museum.
-
B.
Martorell, Spain
Martorell, Spain is a town in Catalonia best known as a major automotive manufacturing hub and home to SEAT’s main production plant.
-
C.
Cuenca, Spain
Cuenca, Spain is a historic city in central Spain renowned for its medieval architecture and dramatic “hanging houses” perched on cliffs above deep river gorges.
-
D.
Granollers, Spain
Granollers, Spain is a town in the province of Barcelona, Catalonia, known as an industrial and commercial center near the Montmeló Circuit de Barcelona-Catalunya.
-
E.
El Masnou, Spain
El Masnou, Spain is a coastal town in the province of Barcelona, Catalonia, known for its Mediterranean beaches and marina.
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe48ca9988190b60ebd09a135194d |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1de232508190803fd2dad21e677f |
completed | April 2, 2026, 7:42 a.m. |
Created at: March 30, 2026, 6:10 p.m.