Triple
T4571008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fala language |
E123028
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object |
Lagarteiru
Lagarteiru is a regional dialect of the Fala language spoken in a small area of Extremadura in western Spain.
|
E454149
|
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: Lagarteiru | Statement: [Fala language, hasDialect, Lagarteiru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lagarteiru Context triple: [Fala language, hasDialect, Lagarteiru]
-
A.
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
B.
Bargara
Bargara is a coastal town in Queensland, Australia, known for its beaches, proximity to the Great Barrier Reef, and role as a gateway to nearby turtle nesting sites at Mon Repos.
-
C.
Guabiraba
Guabiraba is a neighborhood and administrative district located in the northern part of Recife, in the state of Pernambuco, Brazil.
-
D.
Ricinulei
Ricinulei are a small, obscure order of hooded, eyeless arachnids known for their cryptic habits and occurrence mainly in tropical leaf litter and caves.
-
E.
Arana
Arana is a surname most notably associated with American actor Tomas Arana, known for his roles in films such as "Gladiator" and "The Bodyguard."
- 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: Lagarteiru Triple: [Fala language, hasDialect, Lagarteiru]
Generated description
Lagarteiru is a regional dialect of the Fala language spoken in a small area of Extremadura in western Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lagarteiru Target entity description: Lagarteiru is a regional dialect of the Fala language spoken in a small area of Extremadura in western Spain.
-
A.
Lagarto
Lagarto is a municipality in the Brazilian state of Sergipe, known for its agricultural activities and growing regional commerce.
-
B.
Bargara
Bargara is a coastal town in Queensland, Australia, known for its beaches, proximity to the Great Barrier Reef, and role as a gateway to nearby turtle nesting sites at Mon Repos.
-
C.
Guabiraba
Guabiraba is a neighborhood and administrative district located in the northern part of Recife, in the state of Pernambuco, Brazil.
-
D.
Ricinulei
Ricinulei are a small, obscure order of hooded, eyeless arachnids known for their cryptic habits and occurrence mainly in tropical leaf litter and caves.
-
E.
Arana
Arana is a surname most notably associated with American actor Tomas Arana, known for his roles in films such as "Gladiator" and "The Bodyguard."
- 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_69bd46466c7081909d07f36be2d08804 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd58c5afa48190bb8505e2cc16e89f |
completed | March 20, 2026, 2:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3cf5e10819099b2927c6f571673 |
completed | March 20, 2026, 11:10 p.m. |
| NEDg | Description generation | batch_69bdd7f1efd0819089410e63f853175b |
completed | March 20, 2026, 11:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdd86be2c48190af8011a983f26b0d |
completed | March 20, 2026, 11:29 p.m. |
Created at: March 20, 2026, 1:10 p.m.