Triple
T6932007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid–Barcelona railway |
E160456
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Tarragona |
E80788
|
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: Tarragona | Statement: [Madrid–Barcelona railway, servesCity, Tarragona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarragona Context triple: [Madrid–Barcelona railway, servesCity, Tarragona]
-
A.
Tarragona
chosen
Tarragona is a historic port city in northeastern Spain, renowned for its well-preserved Roman ruins and status as a major cultural and economic center in Catalonia.
-
B.
Mataró
Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
-
C.
Lleida
Lleida is a historic city in western Catalonia, Spain, known for its medieval Seu Vella cathedral and role as a regional agricultural and commercial center.
-
D.
Girona
Girona is a historic city in northeastern Catalonia, Spain, known for its well-preserved medieval architecture, walled Old Quarter, and prominent cathedral.
-
E.
Alicante
Alicante is a historic Mediterranean port city in southeastern Spain known for its beaches, castle-topped hill, and role as a major tourist and commercial center.
- 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_69c6884e15208190b9e91487eaafcf85 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da3e58f08190857c14f538448039 |
completed | March 27, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76182c848819081b973683bdd235f |
completed | March 28, 2026, 5:05 a.m. |
Created at: March 27, 2026, 2:27 p.m.