Triple
T13144039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biotrén |
E312288
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Coronel |
E62714
|
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: Coronel | Statement: [Biotrén, connects, Coronel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coronel Context triple: [Biotrén, connects, Coronel]
-
A.
Coronel
chosen
Coronel is a coastal city in south-central Chile known for its historic coal-mining industry and fishing activities along the Pacific Ocean.
-
B.
Le Capitan
Le Capitan is a 1960 French swashbuckling adventure film, based on a novel by Michel Zévaco, in which Jean Marais stars as a valiant swordsman in 17th-century France.
-
C.
Cruz Alta
Cruz Alta is a prominent hilltop viewpoint in the Sintra region of Portugal, known for its panoramic vistas over the surrounding mountains and coastline.
-
D.
Coronel Suárez
Coronel Suárez is a city in Argentina known for its significant German Argentine community and cultural heritage.
-
E.
Admiral Grant
Admiral Grant is a fictional high-ranking naval officer portrayed by actor John Amos.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98bce3678819082a7aa1d83f20592 |
completed | April 10, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f5d809948190aced5ce377402463 |
completed | May 3, 2026, 7:14 a.m. |
Created at: April 9, 2026, 9:10 p.m.