Triple
T13071716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manta Canton |
E329472
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object | Manta |
E241444
|
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: Manta | Statement: [Manta Canton, hasCapital, Manta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manta Context triple: [Manta Canton, hasCapital, Manta]
-
A.
Manta
Manta is a distributed object storage and compute service designed for running parallel computations directly on stored data in the cloud.
-
B.
Manta
Manta is a municipality located in Almeidas Province in the Cundinamarca Department of central Colombia.
-
C.
Manta
chosen
Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
-
D.
Manta
Manta is a flying roller coaster at SeaWorld Orlando that simulates the graceful, gliding motion of a manta ray through a combination of high-speed thrills and aquatic theming.
-
E.
Manta Rota
Manta Rota is a coastal village and popular beach resort in Portugal’s Algarve region, known for its long sandy beach and calm, shallow waters.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ee6130819095d835e7ff6a8c5b |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d60510dc81909e0cba8b63a50d9c |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 9 p.m.