Triple
T15877629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biobío metropolitan area |
E384990
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Lota |
E68423
|
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: Lota | Statement: [Biobío metropolitan area, hasPart, Lota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lota Context triple: [Biobío metropolitan area, hasPart, Lota]
-
A.
Lota
chosen
Lota is a coastal city in southern Chile known historically for its coal mining industry and maritime heritage.
-
B.
Salcha
Salcha is a small unincorporated community in interior Alaska, known for its rural setting along the Tanana River southeast of Fairbanks.
-
C.
Chamkani
Chamkani is a Pashtun tribe traditionally associated with the Karlani tribal confederation in the Afghanistan–Pakistan border region.
-
D.
Kalsa
Kalsa is a historic district of Palermo, Italy, known for its Arab-Norman heritage, medieval streets, and vibrant cultural life.
-
E.
Salar
Salar is a Turkic ethnic group primarily residing in northwestern China, known for speaking the Salar language and practicing Islam.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fec9d4819081efea504e1e3952 |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa950a890819092bc1e8895034593 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:51 a.m.