Triple
T19268123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magangué |
E481842
|
entity |
| Predicate | demonym |
P191
|
FINISHED |
| Object |
Magangueleña
Magangueleña is the term used to refer to a female inhabitant or native of Magangué, a city in the Bolívar Department of Colombia.
|
E1368216
|
NE FINISHED |
How this triple was built (4 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: Magangueleña | Statement: [Magangué, demonym, Magangueleña]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magangueleña Context triple: [Magangué, demonym, Magangueleña]
-
A.
Tantamayo
Tantamayo is a small Andean town in Peru known for its nearby pre-Inca archaeological sites and highland agricultural traditions.
-
B.
Mañegu
Mañegu is a dialect of the Fala language spoken in the border region between Spain and Portugal, known for preserving distinctive features of Galician-Portuguese.
-
C.
Condega
Condega is a municipality in northern Nicaragua known for its agricultural production and traditional pottery within the Estelí region.
-
D.
Cambambe
Cambambe is a town and municipality in Angola notable for its hydroelectric dam on the Cuanza River and its role in regional energy production.
-
E.
Chinandega
Chinandega is a city in northwestern Nicaragua known as a commercial and agricultural hub near the country’s highest volcanoes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Magangueleña Triple: [Magangué, demonym, Magangueleña]
Generated description
Magangueleña is the term used to refer to a female inhabitant or native of Magangué, a city in the Bolívar Department of Colombia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Magangueleña Target entity description: Magangueleña is the term used to refer to a female inhabitant or native of Magangué, a city in the Bolívar Department of Colombia.
-
A.
Tantamayo
Tantamayo is a small Andean town in Peru known for its nearby pre-Inca archaeological sites and highland agricultural traditions.
-
B.
Mañegu
Mañegu is a dialect of the Fala language spoken in the border region between Spain and Portugal, known for preserving distinctive features of Galician-Portuguese.
-
C.
Condega
Condega is a municipality in northern Nicaragua known for its agricultural production and traditional pottery within the Estelí region.
-
D.
Cambambe
Cambambe is a town and municipality in Angola notable for its hydroelectric dam on the Cuanza River and its role in regional energy production.
-
E.
Chinandega
Chinandega is a city in northwestern Nicaragua known as a commercial and agricultural hub near the country’s highest volcanoes.
- F. None of above. chosen
Provenance (5 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb8e1f888190a95f60fa29ca3b98 |
completed | April 20, 2026, 10:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a070e70bf7881909674b254e7968b55 |
completed | May 15, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_6a070f8b626c819083a31123cd6035cf |
completed | May 15, 2026, 12:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a071018f61881908ace460b83dac186 |
completed | May 15, 2026, 12:22 p.m. |
Created at: April 10, 2026, 1:29 p.m.