Triple
T23336829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reus Airport |
E591613
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Tarragona |
—
|
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: Tarragona | Statement: [Reus Airport, serves, Tarragona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarragona Context triple: [Reus Airport, serves, 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.
Tarragona
Tarragona is a coastal municipality in the province of Davao Oriental on the southeastern island of Mindanao in the Philippines.
-
C.
Mataró
Mataró is a coastal city in northeastern Spain known as an important commercial and industrial center on the Mediterranean near Barcelona.
-
D.
Gerona
Gerona is a municipality in the province of Tarlac in the Philippines, known for its primarily agricultural economy and role as a local government unit within the province’s second congressional district.
-
E.
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.
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197f1e0588190bf073b92be0bf9e4 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:17 p.m.