Triple

T29793242
Position Surface form Disambiguated ID Type / Status
Subject Constitución, Buenos Aires E756467 entity
Predicate hasMajorStreet P30026 FINISHED
Object Santiago del Estero Street
Santiago del Estero Street is a principal thoroughfare in the Constitución neighborhood of Buenos Aires, Argentina, known for its urban traffic and local commercial activity.
E2121387 NE FINISHED

How this triple was built (2 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: Santiago del Estero Street | Statement: [Constitución, Buenos Aires, hasMajorStreet, Santiago del Estero Street]
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: Santiago del Estero Street
Triple: [Constitución, Buenos Aires, hasMajorStreet, Santiago del Estero Street]
Generated description
Santiago del Estero Street is a principal thoroughfare in the Constitución neighborhood of Buenos Aires, Argentina, known for its urban traffic and local commercial activity.

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_69f22454583081908927516cb9938d1d completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f674e3b6a88190aa7217ff6f74aa3c completed May 2, 2026, 10:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37bcf02560819091dae088791c9d29 completed June 21, 2026, 10:29 a.m.
NEDg Description generation batch_6a37bd8715048190b1cd7f21e3b39e77 completed June 21, 2026, 10:31 a.m.
NED2 Entity disambiguation (via description) batch_6a37be31ed2c8190b7b287e1e319a182 completed June 21, 2026, 10:34 a.m.
Created at: April 29, 2026, 5:13 p.m.