Triple
T12097459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Comas District |
E288106
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Comas |
E565918
|
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: Comas | Statement: [Comas District, capital, Comas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Comas Context triple: [Comas District, capital, Comas]
-
A.
Comas
chosen
Comas is a populous urban district in northern Lima, Peru, known for its residential neighborhoods and commercial activity within the Greater Lima metropolitan area.
-
B.
Cooma
Cooma is a town in New South Wales, Australia, known as a gateway to the Snowy Mountains and the Snowy Mountains Scheme.
-
C.
Coma Pedrosa
Coma Pedrosa is the tallest mountain in Andorra, known for its rugged alpine terrain and popular hiking routes.
-
D.
Coma
Coma is a 1978 medical thriller film, based on Robin Cook’s novel, that explores a sinister conspiracy involving patients mysteriously falling into comas in a Boston hospital.
-
E.
Coma
"Coma" is a lengthy, hard rock track by Guns N' Roses known for its complex structure, dark themes, and climactic intensity.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91552645c81909aff601ab3d3c0e6 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6708f8881909c4f40a466bc0acf |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.