Triple

T8701508
Position Surface form Disambiguated ID Type / Status
Subject Pohjoisesplanadi E206542 entity
Predicate partOf P40 FINISHED
Object Esplanadi area E748371 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: Esplanadi area | Statement: [Pohjoisesplanadi, partOf, Esplanadi area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Esplanadi area
Context triple: [Pohjoisesplanadi, partOf, Esplanadi area]
  • A. Esplanadi
    Esplanadi is a famous park and promenade in central Helsinki known for its green spaces, cafés, and role as a popular gathering place for locals and tourists.
  • B. Pigalle area
    The Pigalle area is a lively Parisian neighborhood known for its historic cabarets, nightlife, and adult entertainment venues near Montmartre on the Right Bank.
  • C. Picpus neighborhood
    Picpus neighborhood is a residential district in eastern Paris known for its quiet streets, historic sites, and proximity to the Place de la Nation.
  • D. Esplanadi Park chosen
    Esplanadi Park is a popular green promenade and public park in central Helsinki, known for its cafés, cultural events, and role as a key gathering place for locals and tourists.
  • E. Sentier district
    The Sentier district is a historic central Paris neighborhood known for its former textile and garment industry, narrow streets, and more recently its concentration of tech startups and media companies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca83555b6c8190abe930dd397e863b completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc58b38cf88190bfdcbac9c340cb96 completed March 31, 2026, 11:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef41657588190ba6f79c27658dd1b completed April 2, 2026, 10:56 p.m.
Created at: March 30, 2026, 6:34 p.m.