Triple
T13958163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barru Regency |
E335720
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Barru
Barru is a coastal town in South Sulawesi, Indonesia, known as an administrative and economic center in the region.
|
E1071373
|
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: Barru | Statement: [Barru Regency, capital, Barru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barru Context triple: [Barru Regency, capital, Barru]
-
A.
Maicao
Maicao is a Colombian border city in the northeastern La Guajira region, known as a major commercial hub with significant Arab and Wayuu indigenous communities.
-
B.
Yapeyú
Yapeyú is a small town in northeastern Argentina, historically notable as the birthplace of independence leader José de San Martín.
-
C.
Nipomo
Nipomo is a small unincorporated community in California’s Central Coast region, known for its agricultural roots and proximity to the Pacific Ocean in southern San Luis Obispo County.
-
D.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
E.
Caldas
Caldas is a department in west-central Colombia known for its coffee-growing region, mountainous landscapes, and part of the Paisa cultural area.
- 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: Barru Triple: [Barru Regency, capital, Barru]
Generated description
Barru is a coastal town in South Sulawesi, Indonesia, known as an administrative and economic center in the region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barru Target entity description: Barru is a coastal town in South Sulawesi, Indonesia, known as an administrative and economic center in the region.
-
A.
Maicao
Maicao is a Colombian border city in the northeastern La Guajira region, known as a major commercial hub with significant Arab and Wayuu indigenous communities.
-
B.
Yapeyú
Yapeyú is a small town in northeastern Argentina, historically notable as the birthplace of independence leader José de San Martín.
-
C.
Nipomo
Nipomo is a small unincorporated community in California’s Central Coast region, known for its agricultural roots and proximity to the Pacific Ocean in southern San Luis Obispo County.
-
D.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
E.
Caldas
Caldas is a department in west-central Colombia known for its coffee-growing region, mountainous landscapes, and part of the Paisa cultural area.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7a34f08190aa0d88b66154f268 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1d490048190b28cb44dd4ec46c4 |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba5646cb48190acd932f6fbd6fe62 |
completed | May 6, 2026, 8:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba6525d0c8190a1ab15881030c11c |
completed | May 6, 2026, 8:36 p.m. |
Created at: April 9, 2026, 10:17 p.m.