Triple
T1210449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergipe |
E25986
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
|
E137436
|
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: Lagarto | Statement: [Sergipe, hasMunicipality, Lagarto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lagarto Context triple: [Sergipe, hasMunicipality, Lagarto]
-
A.
Guabiraba
Guabiraba is a neighborhood and administrative district located in the northern part of Recife, in the state of Pernambuco, Brazil.
-
B.
Tarantula
Tarantula is an experimental, stream-of-consciousness prose poetry book by Bob Dylan, reflecting his surreal and avant-garde literary style of the 1960s.
-
C.
Grenouilles
Grenouilles is one of the prestigious Grand Cru vineyard sites in the Chablis wine region of Burgundy, France, known for producing high-quality Chardonnay wines.
-
D.
Scorponok
Scorponok is a scorpion-like Decepticon from the Transformers franchise, known for his burrowing attacks and appearances across various series and films.
-
E.
Gecko
Gecko is Mozilla’s open-source web browser engine that powers the rendering and functionality of Firefox and several other applications.
- 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: Lagarto Triple: [Sergipe, hasMunicipality, Lagarto]
Generated description
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lagarto Target entity description: Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
A.
Guabiraba
Guabiraba is a neighborhood and administrative district located in the northern part of Recife, in the state of Pernambuco, Brazil.
-
B.
Tarantula
Tarantula is an experimental, stream-of-consciousness prose poetry book by Bob Dylan, reflecting his surreal and avant-garde literary style of the 1960s.
-
C.
Grenouilles
Grenouilles is one of the prestigious Grand Cru vineyard sites in the Chablis wine region of Burgundy, France, known for producing high-quality Chardonnay wines.
-
D.
Scorponok
Scorponok is a scorpion-like Decepticon from the Transformers franchise, known for his burrowing attacks and appearances across various series and films.
-
E.
Gecko
Gecko is Mozilla’s open-source web browser engine that powers the rendering and functionality of Firefox and several other applications.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bde4670481908c16a3a8c1a54aad |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7f43a12c8190a1ba90eefafd6bbc |
completed | March 7, 2026, 7:40 p.m. |
| NEDg | Description generation | batch_69ac7fbadd08819090ef4a9aff3bef0b |
completed | March 7, 2026, 7:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac8022a6b08190ba6a1448cebe6017 |
completed | March 7, 2026, 7:44 p.m. |
Created at: March 1, 2026, 7:46 p.m.