Triple
T19172278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maranhão |
E469351
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Santa Inês
Santa Inês is a municipality in the Brazilian state of Maranhão, known as a regional commercial and service hub in the eastern part of the state.
|
E1362565
|
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: Santa Inês | Statement: [Maranhão, hasCity, Santa Inês]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Inês Context triple: [Maranhão, hasCity, Santa Inês]
-
A.
Santa Ifigênia
Santa Ifigênia is a historic central neighborhood in São Paulo, Brazil, known for its bustling electronics commerce and proximity to major downtown landmarks.
-
B.
Santa Ines
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
-
C.
Santa Tereza
Santa Tereza is a small wine-producing town in Brazil’s Serra Gaúcha region, known for its Italian heritage and scenic mountain landscapes.
-
D.
Santa Isabel
Santa Isabel is an urban neighborhood within the Carabayllo district of Lima, Peru.
-
E.
Santa Isabel
Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
- 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: Santa Inês Triple: [Maranhão, hasCity, Santa Inês]
Generated description
Santa Inês is a municipality in the Brazilian state of Maranhão, known as a regional commercial and service hub in the eastern part of the state.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santa Inês Target entity description: Santa Inês is a municipality in the Brazilian state of Maranhão, known as a regional commercial and service hub in the eastern part of the state.
-
A.
Santa Ifigênia
Santa Ifigênia is a historic central neighborhood in São Paulo, Brazil, known for its bustling electronics commerce and proximity to major downtown landmarks.
-
B.
Santa Ines
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
-
C.
Santa Tereza
Santa Tereza is a small wine-producing town in Brazil’s Serra Gaúcha region, known for its Italian heritage and scenic mountain landscapes.
-
D.
Santa Isabel
Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
-
E.
Santa Isabel
Santa Isabel is an urban neighborhood within the Carabayllo district of Lima, Peru.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f16481948190973067eb854da237 |
completed | April 20, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a06f8ac3304819091fa43ec3f7573b6 |
completed | May 15, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_6a06f9cc015c8190a87518c058e2a233 |
completed | May 15, 2026, 10:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a06fa3aa394819096ef4b6f3dddba1a |
completed | May 15, 2026, 10:49 a.m. |
Created at: April 10, 2026, 12:06 p.m.