Triple
T1196746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ceará |
E25684
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
|
E157168
|
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: Caucaia | Statement: [Ceará, hasCity, Caucaia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caucaia Context triple: [Ceará, hasCity, Caucaia]
-
A.
Igarassu
Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
-
B.
Parnamirim
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
-
C.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
D.
Currais Novos
Currais Novos is a municipality in the interior of the Brazilian state of Rio Grande do Norte, known for its semi-arid climate, livestock farming, and mineral resources.
-
E.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
- 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: Caucaia Triple: [Ceará, hasCity, Caucaia]
Generated description
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caucaia Target entity description: Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
A.
Igarassu
Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
-
B.
Parnamirim
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
-
C.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
D.
Currais Novos
Currais Novos is a municipality in the interior of the Brazilian state of Rio Grande do Norte, known for its semi-arid climate, livestock farming, and mineral resources.
-
E.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
- 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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd9a305c819091513394f1b67784 |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd46eefa48190baebc12fdf916941 |
completed | March 8, 2026, 1:44 a.m. |
| NEDg | Description generation | batch_69acd517cfec8190ba294bb890d0b4b1 |
completed | March 8, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acd58946588190a5e92c62b585cdfc |
completed | March 8, 2026, 1:48 a.m. |
Created at: March 1, 2026, 7:46 p.m.