Triple
T9587798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aragon |
E231334
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Teruel |
E574615
|
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: Teruel | Statement: [Aragon, containsCity, Teruel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teruel Context triple: [Aragon, containsCity, Teruel]
-
A.
Jaén
Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
-
B.
Jaén
Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
-
C.
Almería
Almería is a coastal city and province in southeastern Spain known for its arid climate, historic Alcazaba fortress, and extensive greenhouse agriculture.
-
D.
city of Teruel
chosen
The city of Teruel is the capital of Spain’s Teruel province, known for its Mudéjar architecture and historic medieval heritage.
-
E.
Baeza
Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
- 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_69ca848161688190a68d514a0a9d5129 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99f01ca08190afaa44645a58e918 |
completed | April 1, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d29999e9248190b2f1900fa7ad3da5 |
completed | April 5, 2026, 5:19 p.m. |
Created at: March 30, 2026, 8:06 p.m.