Triple
T8929621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pont del Diable |
E212617
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Martorell |
E767182
|
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: Martorell | Statement: [Pont del Diable, locatedIn, Martorell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martorell Context triple: [Pont del Diable, locatedIn, Martorell]
-
A.
Martorell
chosen
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
-
B.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
-
C.
Ampurias
Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
-
D.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
E.
Mataró
Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
- 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_69ca8395c438819087d7cb844ab5990c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc667470308190a75ba63de803e3a2 |
completed | April 1, 2026, 12:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc93587b081908e23c2a8c01b9516 |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:57 p.m.