Triple
T6008446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mato Grosso do Sul |
E133771
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Três Lagoas
Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
|
E575510
|
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: Três Lagoas | Statement: [Mato Grosso do Sul, hasCity, Três Lagoas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Três Lagoas Context triple: [Mato Grosso do Sul, hasCity, Três Lagoas]
-
A.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
B.
Mourão
Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
-
C.
Dourados
Dourados is a major agricultural and commercial city in the Brazilian state of Mato Grosso do Sul, known as an important regional economic and educational center.
-
D.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
E.
Campo Grande
Campo Grande is the capital city of Brazil’s Mato Grosso do Sul state and a key urban and transportation hub for visitors heading into the Pantanal wetlands.
- 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: Três Lagoas Triple: [Mato Grosso do Sul, hasCity, Três Lagoas]
Generated description
Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Três Lagoas Target entity description: Três Lagoas is a Brazilian city in the state of Mato Grosso do Sul known for its strong pulp and paper industry and growing industrial sector.
-
A.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
B.
Mourão
Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
-
C.
Dourados
Dourados is a major agricultural and commercial city in the Brazilian state of Mato Grosso do Sul, known as an important regional economic and educational center.
-
D.
Campo Grande
Campo Grande is a neighborhood in the city of Recife, Brazil.
-
E.
Campo Grande
Campo Grande is the capital city of Brazil’s Mato Grosso do Sul state and a key urban and transportation hub for visitors heading into the Pantanal wetlands.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f154ca481909431baf4feecc16d |
completed | March 22, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16e985f34819091d28debdd5a2950 |
completed | March 23, 2026, 4:47 p.m. |
| NEDg | Description generation | batch_69c1c27875348190a9239c1bfff02c5b |
completed | March 23, 2026, 10:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1c67eeb908190abaa591db960ae9d |
completed | March 23, 2026, 11:02 p.m. |
Created at: March 22, 2026, 4:06 p.m.