Triple
T18558182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ignatian Way |
E453559
|
entity |
| Predicate | endPoint |
P390
|
FINISHED |
| Object | Manresa |
—
|
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: Manresa | Statement: [Ignatian Way, endPoint, Manresa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manresa Context triple: [Ignatian Way, endPoint, Manresa]
-
A.
Manresa
chosen
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
-
B.
Begur
Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
-
C.
Vallmoll
Vallmoll is a small municipality in the province of Tarragona, within the autonomous community of Catalonia in northeastern Spain.
-
D.
Vilassar de Mar
Vilassar de Mar is a coastal town and municipality on the Mediterranean in the Maresme comarca of Catalonia, Spain, known for its beaches and residential character.
-
E.
Banyoles
Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
- 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53808028c8190825630525d84a29b |
completed | April 19, 2026, 8:16 p.m. |
Created at: April 10, 2026, 11:41 a.m.