Triple
T1868305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Río Muni |
E34972
|
entity |
| Predicate | containsProvince |
P11085
|
FINISHED |
| Object |
Litoral
Litoral is a coastal province of Equatorial Guinea located on the mainland between Cameroon and Gabon along the Gulf of Guinea.
|
E207848
|
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: Litoral | Statement: [Río Muni, containsProvince, Litoral]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Litoral Context triple: [Río Muni, containsProvince, Litoral]
-
A.
Molles
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
-
B.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
-
C.
Horizonte
Horizonte is a municipality in the state of Ceará in northeastern Brazil, known for its growing industrial sector and proximity to the Fortaleza metropolitan area.
-
D.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
E.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
- 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: Litoral Triple: [Río Muni, containsProvince, Litoral]
Generated description
Litoral is a coastal province of Equatorial Guinea located on the mainland between Cameroon and Gabon along the Gulf of Guinea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Litoral Target entity description: Litoral is a coastal province of Equatorial Guinea located on the mainland between Cameroon and Gabon along the Gulf of Guinea.
-
A.
Molles
Molles is a small commune in central France, located in the Allier department within the Auvergne-Rhône-Alpes region.
-
B.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
-
C.
Horizonte
Horizonte is a municipality in the state of Ceará in northeastern Brazil, known for its growing industrial sector and proximity to the Fortaleza metropolitan area.
-
D.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
-
E.
Barra
Barra is the surname of Mary Barra, the prominent American business executive and CEO of General Motors.
- 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb0b6ac108190921c197abc5ab5b5 |
completed | March 7, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69add1d8dd8881909189029a047bc2b4 |
completed | March 8, 2026, 7:45 p.m. |
| NEDg | Description generation | batch_69add28b804c8190a625e5d1405c59be |
completed | March 8, 2026, 7:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69add35731588190a13c969490ca2c09 |
completed | March 8, 2026, 7:51 p.m. |
Created at: March 4, 2026, 7:34 p.m.