Triple
T17316325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cerro Negro |
E420433
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | León |
—
|
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: León | Statement: [Cerro Negro, nearCity, León]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: León Context triple: [Cerro Negro, nearCity, León]
-
A.
León
chosen
León is a historic city in western Nicaragua known for its colonial architecture, vibrant cultural life, and role as an intellectual and political center of the country.
-
B.
León
León is a major industrial and commercial city in central Mexico, renowned especially for its leather and footwear production.
-
C.
León
León is a historic city and former kingdom in northwestern Spain, renowned for its medieval architecture and significant role in the formation of the Spanish state.
-
D.
León
León is a historic and successful Mexican professional football club known for its multiple Liga MX titles and passionate fan base.
-
E.
León
León is a masculine given name of Spanish origin, commonly used in Spanish-speaking countries and derived from the word for "lion."
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399d2fcc81909916302f141e236b |
completed | April 19, 2026, 2:10 a.m. |
Created at: April 10, 2026, 5:43 a.m.