Triple
T9433071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cortes of Aragon |
E227431
|
entity |
| Predicate | meetsIn |
P40
|
FINISHED |
| Object | Zaragoza |
E55920
|
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: Zaragoza | Statement: [Cortes of Aragon, meetsIn, Zaragoza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zaragoza Context triple: [Cortes of Aragon, meetsIn, Zaragoza]
-
A.
Zaragoza
Zaragoza is a metro station on Mexico City’s Line 1 that serves as a key eastern access point to the city’s rapid transit network.
-
B.
Zaragoza
chosen
Zaragoza is a historic city in northeastern Spain, known for landmarks like the Basilica del Pilar and its role as a major cultural and economic center in the Aragon region.
-
C.
Burgos
Burgos is a historic city in northern Spain known for its medieval architecture and its prominent role during the Spanish Civil War.
-
D.
Burgos
Burgos is a small coastal municipality on the northern tip of Siargao Island in the Philippines, known for its quiet beaches and surf spots.
-
E.
Vitoria-Gasteiz
Vitoria-Gasteiz is a historic city in northern Spain that serves as the administrative capital of the Basque Autonomous Community and is known for its well-preserved medieval quarter and extensive green spaces.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e61a114819081fc4a2ad39c96ba |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d190cc06d48190b437edfc4fee8cce |
completed | April 4, 2026, 10:29 p.m. |
Created at: March 30, 2026, 7:49 p.m.