Triple
T4204627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buenos Aires Province |
E86153
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Bahía Blanca
Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
|
E431330
|
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: Bahía Blanca | Statement: [Buenos Aires Province, containsCity, Bahía Blanca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bahía Blanca Context triple: [Buenos Aires Province, containsCity, Bahía Blanca]
-
A.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
-
B.
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.
-
C.
Gualeguaychú
Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
-
D.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
-
E.
Tandil
Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
- 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: Bahía Blanca Triple: [Buenos Aires Province, containsCity, Bahía Blanca]
Generated description
Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bahía Blanca Target entity description: Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
-
A.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
-
B.
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.
-
C.
Gualeguaychú
Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
-
D.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
-
E.
Tandil
Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
- 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_69b5d05b964881908d7d52b70cec2dcc |
completed | March 14, 2026, 9:17 p.m. |
| NEDg | Description generation | batch_69b5d24c25088190b002aa4231d5f5c4 |
completed | March 14, 2026, 9:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5d2c040a081909c74d1a6cbf54dd6 |
completed | March 14, 2026, 9:27 p.m. |
Created at: March 9, 2026, 3:49 p.m.