Triple
T10066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coquimbo Region |
E204
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | La Serena |
E503
|
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: La Serena | Statement: [Coquimbo Region, capital, La Serena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Serena Context triple: [Coquimbo Region, capital, La Serena]
-
A.
La Serena
chosen
La Serena is a coastal city in northern Chile known for its colonial architecture, beaches, and role as a gateway to major astronomical observatories in the region.
-
B.
Santiago
Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
-
C.
Ovalle
Ovalle is a Chilean city known as an agricultural and commercial center in the north-central part of the country.
-
D.
Lima
Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
-
E.
Buenos Aires
Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a23ff2f0508190806663ab2463cd41 |
completed | Feb. 28, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a25aac4900819093912edb0121ff9d |
completed | Feb. 28, 2026, 3:02 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.