Triple
T4204650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buenos Aires Province |
E86153
|
entity |
| Predicate | hasCoastalResort |
P10436
|
FINISHED |
| Object |
Pinamar
Pinamar is a popular seaside resort city on Argentina’s Atlantic coast, known for its pine forests, wide beaches, and upscale vacation atmosphere.
|
E422271
|
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: Pinamar | Statement: [Buenos Aires Province, hasCoastalResort, Pinamar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pinamar Context triple: [Buenos Aires Province, hasCoastalResort, Pinamar]
-
A.
Comodoro Rivadavia
Comodoro Rivadavia is a coastal city in southern Argentina known as a key oil industry hub and one of the main urban centers of Patagonia.
-
B.
Ushuaia
Ushuaia is the southernmost city in the world, located in Argentina’s Tierra del Fuego and serving as a major gateway to Antarctic voyages.
-
C.
Bariloche
Bariloche is a popular Argentine city in the Andean region known for its lakes, mountains, skiing, and Swiss-style alpine architecture.
-
D.
Tandil
Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
-
E.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
- 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: Pinamar Triple: [Buenos Aires Province, hasCoastalResort, Pinamar]
Generated description
Pinamar is a popular seaside resort city on Argentina’s Atlantic coast, known for its pine forests, wide beaches, and upscale vacation atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pinamar Target entity description: Pinamar is a popular seaside resort city on Argentina’s Atlantic coast, known for its pine forests, wide beaches, and upscale vacation atmosphere.
-
A.
Comodoro Rivadavia
Comodoro Rivadavia is a coastal city in southern Argentina known as a key oil industry hub and one of the main urban centers of Patagonia.
-
B.
Ushuaia
Ushuaia is the southernmost city in the world, located in Argentina’s Tierra del Fuego and serving as a major gateway to Antarctic voyages.
-
C.
Bariloche
Bariloche is a popular Argentine city in the Andean region known for its lakes, mountains, skiing, and Swiss-style alpine architecture.
-
D.
Tandil
Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
-
E.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0382eafc8190946bf45bf28095dd |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b596258db88190aed602eeb2323fee |
completed | March 14, 2026, 5:08 p.m. |
| NEDg | Description generation | batch_69b59695c99481909a061751eaccbb25 |
completed | March 14, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b59a568e288190a87ba03b181f27df |
completed | March 14, 2026, 5:26 p.m. |
Created at: March 9, 2026, 3:49 p.m.