Triple
T20303325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Collón Curá Department |
E505540
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Sañicó
Sañicó is a small rural locality in the Collón Curá Department of Neuquén Province in Argentine Patagonia.
|
E1423293
|
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: Sañicó | Statement: [Collón Curá Department, hasSettlement, Sañicó]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sañicó Context triple: [Collón Curá Department, hasSettlement, Sañicó]
-
A.
Briceño
Briceño is a municipality in Colombia’s Cundinamarca Department, located within the Sabana Centro Province near the Bogotá metropolitan area.
-
B.
Tamuín
Tamuín is a municipality in the Mexican state of San Luis Potosí, known for its Huastec cultural heritage and proximity to important archaeological and natural sites.
-
C.
Talancón
Talancón is a Spanish-language surname most notably borne by Mexican actress and model Ana Claudia Talancón.
-
D.
Requena
Requena is a historic inland town in Spain’s Valencian Community, known for its wine production and well-preserved medieval quarter.
-
E.
Requena
Requena is a small Peruvian city in the Loreto region, known as a remote Amazonian river port and gateway to surrounding rainforest communities.
- 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: Sañicó Triple: [Collón Curá Department, hasSettlement, Sañicó]
Generated description
Sañicó is a small rural locality in the Collón Curá Department of Neuquén Province in Argentine Patagonia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sañicó Target entity description: Sañicó is a small rural locality in the Collón Curá Department of Neuquén Province in Argentine Patagonia.
-
A.
Briceño
Briceño is a municipality in Colombia’s Cundinamarca Department, located within the Sabana Centro Province near the Bogotá metropolitan area.
-
B.
Tamuín
Tamuín is a municipality in the Mexican state of San Luis Potosí, known for its Huastec cultural heritage and proximity to important archaeological and natural sites.
-
C.
Talancón
Talancón is a Spanish-language surname most notably borne by Mexican actress and model Ana Claudia Talancón.
-
D.
Requena
Requena is a historic inland town in Spain’s Valencian Community, known for its wine production and well-preserved medieval quarter.
-
E.
Requena
Requena is a small Peruvian city in the Loreto region, known as a remote Amazonian river port and gateway to surrounding rainforest communities.
- 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6773e0864819095d272659cd2074d |
completed | April 20, 2026, 6:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a086119b9808190ba5ebb80b43953d3 |
completed | May 16, 2026, 12:20 p.m. |
| NEDg | Description generation | batch_6a0861b954888190a6305aa805aa97ff |
completed | May 16, 2026, 12:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0862237d148190b4e12c878ca9c7a2 |
completed | May 16, 2026, 12:25 p.m. |
Created at: April 16, 2026, 11:17 a.m.