Triple
T25202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Serena |
E503
|
entity |
| Predicate | country |
P26
|
FINISHED |
| Object | Chile |
E203
|
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: Chile | Statement: [La Serena, country, Chile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chile Context triple: [La Serena, country, Chile]
-
A.
Chile
chosen
Chile is a long, narrow South American country stretching along the Pacific coast, renowned for its diverse climates, stable economy, and world-class astronomical observatories.
-
B.
Argentina
Argentina is a large South American nation known for its diverse landscapes from the Andes to the Pampas, its vibrant culture including tango and football, and its capital city Buenos Aires.
-
C.
Peru
Peru is a South American country known for its rich Inca heritage, diverse landscapes from Andes mountains to Amazon rainforest, and the iconic archaeological site of Machu Picchu.
-
D.
Bolivia
Bolivia is a landlocked country in central South America known for its diverse indigenous cultures, Andean and Amazonian landscapes, and administrative capitals La Paz and Sucre.
-
E.
Colombia
Colombia is a transcontinental country in northern South America, known for its diverse landscapes from Andes mountains to Amazon rainforest, rich cultural heritage, and major cities like Bogotá and Medellín.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a2466d166881908bd8513c8d09fe8d |
completed | Feb. 28, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2e0f1571481908459c38b1e949b3f |
completed | Feb. 28, 2026, 12:34 p.m. |
Created at: Feb. 28, 2026, 1:34 a.m.