Triple
T4937331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Loa Airport |
E110843
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Calama |
E34573
|
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: Calama | Statement: [El Loa Airport, locatedIn, Calama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calama Context triple: [El Loa Airport, locatedIn, Calama]
-
A.
Calama
chosen
Calama is a city in northern Chile known as a key mining center and gateway to the Atacama Desert.
-
B.
Puerto Varas
Puerto Varas is a picturesque lakeside city in southern Chile’s Los Lagos Region, known for its German-influenced architecture and views of the Osorno and Calbuco volcanoes.
-
C.
Pichilemu
Pichilemu is a coastal Chilean city renowned as a major surfing destination and seaside resort on the Pacific Ocean.
-
D.
Melipeuco
Melipeuco is a small Andean foothill town and commune in southern Chile known for its proximity to Conguillío National Park and the Llaima volcano.
-
E.
La Serena
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.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70872270819080769dad972681ef |
completed | March 20, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf28f128f48190a8bdaf77b6ca6e15 |
completed | March 21, 2026, 11:25 p.m. |
Created at: March 20, 2026, 1:31 p.m.