Triple

T30182704
Position Surface form Disambiguated ID Type / Status
Subject Western major district of Helsinki E767245 entity
Predicate containsAdministrativeTerritorialEntity P747 FINISHED
Object Pitäjänmäki
Pitäjänmäki is a district in western Helsinki, Finland, known for its mix of residential areas, business parks, and good transport connections.
E1945089 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: Pitäjänmäki | Statement: [Western major district of Helsinki, containsAdministrativeTerritorialEntity, Pitäjänmäki]
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: Pitäjänmäki
Triple: [Western major district of Helsinki, containsAdministrativeTerritorialEntity, Pitäjänmäki]
Generated description
Pitäjänmäki is a district in western Helsinki, Finland, known for its mix of residential areas, business parks, and good transport connections.

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_69f2247cc3d88190811dec3face94bf5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f42c4708190accbdb72ae9c9816 completed May 2, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a292aed33588190a97f5538bca52a6a completed June 10, 2026, 9:14 a.m.
NEDg Description generation batch_6a292bef9bb88190970e5b8830feda22 completed June 10, 2026, 9:18 a.m.
NED2 Entity disambiguation (via description) batch_6a292c909f1881908aa4f58707ae16da completed June 10, 2026, 9:21 a.m.
Created at: April 29, 2026, 7:26 p.m.