Triple

T1911139
Position Surface form Disambiguated ID Type / Status
Subject Puerto Madero E38112 entity
Predicate hasPart P35 FINISHED
Object Dique 1
Dique 1 is one of the renovated dock basins in Buenos Aires’ Puerto Madero district, now surrounded by modern residential, commercial, and leisure developments along the waterfront.
 E211529 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: Dique 1 | Statement: [Puerto Madero, hasPart, Dique 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dique 1
Context triple: [Puerto Madero, hasPart, Dique 1]
  • A. Dike
    Dike is the Greek goddess of justice and moral order, often depicted as a guardian of human righteousness and fairness.
  • B. Dickson
    Dickson is a Scottish surname borne by various notable individuals across fields such as music, literature, and public life.
  • C. Дьокуускай
    Дьокуускай is the Sakha (Yakut) name for Yakutsk, the capital city of Russia’s Sakha Republic in northeastern Siberia.
  • D. Dix
    Dix is the surname of Dorothea Dix, the 19th-century American social reformer known for her pioneering work in mental health care and prison reform.
  • E. Pescina
    Pescina is a small town in the Abruzzo region of central Italy, known as the birthplace of Cardinal Mazarin and for its historic medieval architecture.
  • 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: Dique 1
Triple: [Puerto Madero, hasPart, Dique 1]
Generated description
Dique 1 is one of the renovated dock basins in Buenos Aires’ Puerto Madero district, now surrounded by modern residential, commercial, and leisure developments along the waterfront.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dique 1
Target entity description: Dique 1 is one of the renovated dock basins in Buenos Aires’ Puerto Madero district, now surrounded by modern residential, commercial, and leisure developments along the waterfront.
  • A. Dike
    Dike is the Greek goddess of justice and moral order, often depicted as a guardian of human righteousness and fairness.
  • B. Dickson
    Dickson is a Scottish surname borne by various notable individuals across fields such as music, literature, and public life.
  • C. Дьокуускай
    Дьокуускай is the Sakha (Yakut) name for Yakutsk, the capital city of Russia’s Sakha Republic in northeastern Siberia.
  • D. Dix
    Dix is the surname of Dorothea Dix, the 19th-century American social reformer known for her pioneering work in mental health care and prison reform.
  • E. Pescina
    Pescina is a small town in the Abruzzo region of central Italy, known as the birthplace of Cardinal Mazarin and for its historic medieval architecture.
  • 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_69a8862a26088190aae5243695aeefc0 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1b88db48190a9229a7416054a85 completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeaffbc2c81908303548fac82ff52 completed March 8, 2026, 9:32 p.m.
NEDg Description generation batch_69adeb8c221881909beb938ea9b8a56b completed March 8, 2026, 9:35 p.m.
NED2 Entity disambiguation (via description) batch_69adec388ed08190aa9631c0919923bf completed March 8, 2026, 9:38 p.m.
Created at: March 4, 2026, 7:35 p.m.