Triple

T952817
Position Surface form Disambiguated ID Type / Status
Subject Tallinn E20558 entity
Predicate hasDistrict P459 FINISHED
Object Nõmme
Nõmme is a leafy, villa-filled suburban district of Tallinn known for its pine forests, wooden architecture, and small-town atmosphere within the capital.
 E117129 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: Nõmme | Statement: [Tallinn, hasDistrict, Nõmme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nõmme
Context triple: [Tallinn, hasDistrict, Nõmme]
  • A. Mustamäe
    Mustamäe is a residential district in the western part of Tallinn, Estonia, known for its Soviet-era apartment blocks and proximity to universities and technology hubs.
  • B. Viedma
    Viedma is a city in northern Patagonia and one of the oldest settlements in Argentina, serving as the capital of Río Negro Province.
  • C. Terjola
    Terjola is a small city in western Georgia known for its location in the Imereti region and its surrounding wine-producing areas.
  • D. Oulainen
    Oulainen is a small town and municipality in Northern Ostrobothnia, Finland, known for its rural character and local cultural events.
  • E. Hiiumaa
    Hiiumaa is Estonia’s second-largest island, located in the Baltic Sea and known for its unspoiled nature, lighthouses, and quiet rural landscapes.
  • 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: Nõmme
Triple: [Tallinn, hasDistrict, Nõmme]
Generated description
Nõmme is a leafy, villa-filled suburban district of Tallinn known for its pine forests, wooden architecture, and small-town atmosphere within the capital.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nõmme
Target entity description: Nõmme is a leafy, villa-filled suburban district of Tallinn known for its pine forests, wooden architecture, and small-town atmosphere within the capital.
  • A. Mustamäe
    Mustamäe is a residential district in the western part of Tallinn, Estonia, known for its Soviet-era apartment blocks and proximity to universities and technology hubs.
  • B. Viedma
    Viedma is a city in northern Patagonia and one of the oldest settlements in Argentina, serving as the capital of Río Negro Province.
  • C. Terjola
    Terjola is a small city in western Georgia known for its location in the Imereti region and its surrounding wine-producing areas.
  • D. Oulainen
    Oulainen is a small town and municipality in Northern Ostrobothnia, Finland, known for its rural character and local cultural events.
  • E. Hiiumaa
    Hiiumaa is Estonia’s second-largest island, located in the Baltic Sea and known for its unspoiled nature, lighthouses, and quiet rural landscapes.
  • 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_69a493b0f2fc81908cd227480a5356a1 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b3d8f2e0819097554a301f8aa70f completed March 1, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac25845f588190b0f60754636a88d0 completed March 7, 2026, 1:17 p.m.
NEDg Description generation batch_69ac2674f5b88190bb3416a249a63982 completed March 7, 2026, 1:21 p.m.
NED2 Entity disambiguation (via description) batch_69ac2704de788190857a3104180ccd21 completed March 7, 2026, 1:24 p.m.
Created at: March 1, 2026, 7:40 p.m.