Triple
T6298959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iberia Regional |
E141201
|
entity |
| Predicate | headquarters |
P62
|
FINISHED |
| Object | Valencia |
E13494
|
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: Valencia | Statement: [Iberia Regional, headquarters, Valencia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valencia Context triple: [Iberia Regional, headquarters, Valencia]
-
A.
Valencia
chosen
Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
-
B.
Valencia
Valencia is a municipality in the Philippine province of Negros Oriental known for its cool climate, geothermal energy resources, and natural attractions such as waterfalls and mountain landscapes.
-
C.
Valencia
Valencia is a city in Ecuador that serves as the capital of Los Ríos Province’s Valencia Canton and is known for its agricultural surroundings and tropical climate.
-
D.
Valencia
Valencia is a major industrial and commercial city in north-central Venezuela and the capital of Carabobo state.
-
E.
Valencia
Valencia was the original working title for the 2016 psychological thriller film "10 Cloverfield Lane."
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0643ddaa48190b3ea8061fc1d9dc4 |
completed | March 22, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6537fc2ac8190a05779363ed2b3eb |
completed | March 27, 2026, 9:53 a.m. |
Created at: March 22, 2026, 4:27 p.m.