Triple

T12851812
Position Surface form Disambiguated ID Type / Status
Subject Atlantic coast of Argentina E307340 entity
Predicate hasPort P35 FINISHED
Object Quequén
Quequén is a coastal town in Buenos Aires Province, Argentina, known for its deep-water port and beaches along the Atlantic Ocean.
 E1007424 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: Quequén | Statement: [Atlantic coast of Argentina, hasPort, Quequén]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Quequén
Context triple: [Atlantic coast of Argentina, hasPort, Quequén]
  • A. La Paz batchoy
    La Paz batchoy is a Filipino noodle soup from Iloilo made with egg noodles, pork offal, crushed chicharrón, and savory broth, regarded as a signature Ilonggo comfort food.
  • B. Pastaza
    Pastaza is a large, sparsely populated province in eastern Ecuador known for its Amazon rainforest, rich biodiversity, and indigenous communities.
  • C. Canillejas
    Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
  • D. Cachopo
    Cachopo is a rural village and parish in the hills of the Algarve region of southern Portugal, known for its traditional architecture and scenic landscapes.
  • E. Gurabeña
    Gurabeña is the Spanish term for a female resident or native of the municipality of Gurabo in Puerto Rico.
  • 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: Quequén
Triple: [Atlantic coast of Argentina, hasPort, Quequén]
Generated description
Quequén is a coastal town in Buenos Aires Province, Argentina, known for its deep-water port and beaches along the Atlantic Ocean.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Quequén
Target entity description: Quequén is a coastal town in Buenos Aires Province, Argentina, known for its deep-water port and beaches along the Atlantic Ocean.
  • A. La Paz batchoy
    La Paz batchoy is a Filipino noodle soup from Iloilo made with egg noodles, pork offal, crushed chicharrón, and savory broth, regarded as a signature Ilonggo comfort food.
  • B. Pastaza
    Pastaza is a large, sparsely populated province in eastern Ecuador known for its Amazon rainforest, rich biodiversity, and indigenous communities.
  • C. Canillejas
    Canillejas is a Madrid Metro station serving the Canillejas neighborhood in the San Blas-Canillejas district of Madrid, Spain.
  • D. Cachopo
    Cachopo is a rural village and parish in the hills of the Algarve region of southern Portugal, known for its traditional architecture and scenic landscapes.
  • E. Gurabeña
    Gurabeña is the Spanish term for a female resident or native of the municipality of Gurabo in Puerto Rico.
  • 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_69d7bdf5e7cc8190be357278bc5ba3bb completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97020eacc81909357b3398d17dc49 completed April 10, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69ba79918819093e047ce22191923 completed May 3, 2026, 12:49 a.m.
NEDg Description generation batch_69f69c8469548190b05d8fa010e0ca13 completed May 3, 2026, 12:53 a.m.
NED2 Entity disambiguation (via description) batch_69f69d4ef7988190890f8a62280aa673 completed May 3, 2026, 12:56 a.m.
Created at: April 9, 2026, 5:36 p.m.