Triple
T5848430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alto del Carmen |
E129768
|
entity |
| Predicate | hasProvinceCapital |
P3433
|
FINISHED |
| Object | Vallenar |
E180579
|
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: Vallenar | Statement: [Alto del Carmen, hasProvinceCapital, Vallenar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vallenar Context triple: [Alto del Carmen, hasProvinceCapital, Vallenar]
-
A.
Vallenar
chosen
Vallenar is a city in northern Chile known as an agricultural and mining center in the Atacama Desert.
-
B.
Puerto Montt
Puerto Montt is a port city in southern Chile, serving as a key gateway to the Patagonian fjords and the Chilean Lake District.
-
C.
San Luis de Quillota
San Luis de Quillota is a Chilean professional football club based in the city of Quillota.
-
D.
Coyhaique
Coyhaique is a small city in Chilean Patagonia known as a gateway to the remote Aysén Region, surrounded by mountains, rivers, and forests.
-
E.
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.
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035145a0c8190941945a83a3f2416 |
completed | March 22, 2026, 6:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c124f45d8c8190a757c82abd85c514 |
completed | March 23, 2026, 11:33 a.m. |
Created at: March 22, 2026, 3:55 p.m.