Triple
T5573505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potosí Department |
E146260
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Llallagua
Llallagua is a mining town in Bolivia known historically for its rich tin deposits and labor movements.
|
E533526
|
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: Llallagua | Statement: [Potosí Department, contains, Llallagua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Llallagua Context triple: [Potosí Department, contains, Llallagua]
-
A.
Laguna Cejar
Laguna Cejar is a striking turquoise, salt-rich lagoon in Chile’s Atacama Desert, famous for its high salinity that lets visitors float effortlessly on its surface.
-
B.
Lago Ranco
Lago Ranco is a small town in southern Chile known for its scenic setting on the shores of Lake Ranco in the Los Ríos Region.
-
C.
Curanilahue
Curanilahue is a Chilean city and commune in the Biobío Region, known historically for its coal mining industry and location in the Arauco Province.
-
D.
Laguna del Maule
Laguna del Maule is a high-altitude volcanic lake and reservoir in the Andes of central Chile, known for its geothermal activity and role in regional hydropower and water supply.
-
E.
Lake Junín
Lake Junín is a large high-altitude Andean lake in central Peru, known for its unique wetland ecosystem and endemic bird species.
- 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: Llallagua Triple: [Potosí Department, contains, Llallagua]
Generated description
Llallagua is a mining town in Bolivia known historically for its rich tin deposits and labor movements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Llallagua Target entity description: Llallagua is a mining town in Bolivia known historically for its rich tin deposits and labor movements.
-
A.
Laguna Cejar
Laguna Cejar is a striking turquoise, salt-rich lagoon in Chile’s Atacama Desert, famous for its high salinity that lets visitors float effortlessly on its surface.
-
B.
Lago Ranco
Lago Ranco is a small town in southern Chile known for its scenic setting on the shores of Lake Ranco in the Los Ríos Region.
-
C.
Curanilahue
Curanilahue is a Chilean city and commune in the Biobío Region, known historically for its coal mining industry and location in the Arauco Province.
-
D.
Laguna del Maule
Laguna del Maule is a high-altitude volcanic lake and reservoir in the Andes of central Chile, known for its geothermal activity and role in regional hydropower and water supply.
-
E.
Lake Junín
Lake Junín is a large high-altitude Andean lake in central Peru, known for its unique wetland ecosystem and endemic bird species.
- 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_69c008ffed108190a084602227af6157 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02052dd0481909aba6863831357eb |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c02852a6fc8190a543508ab3237f95 |
completed | March 22, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69c0430e51fc819084706f52a815350a |
completed | March 22, 2026, 7:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c046e54a20819080eef5179f206314 |
completed | March 22, 2026, 7:45 p.m. |
Created at: March 22, 2026, 3:37 p.m.