Triple

T29386009

Position	Surface form	Disambiguated ID	Type / Status
Subject	Southern Helsinki	`E745254`	entity
Predicate	containsAdministrativeDivision	`P747`	FINISHED
Object	Munkkivuori neighborhood Munkkivuori neighborhood is a residential district in southern Helsinki known for its post-war architecture, green surroundings, and local shopping center.	`E1880550`	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: Munkkivuori neighborhood | Statement: [Southern Helsinki, containsAdministrativeDivision, Munkkivuori neighborhood]

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: Munkkivuori neighborhood
Triple: [Southern Helsinki, containsAdministrativeDivision, Munkkivuori neighborhood]

Generated description

Munkkivuori neighborhood is a residential district in southern Helsinki known for its post-war architecture, green surroundings, and local shopping center.

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_69f0a79cfd5481909b4dde750cb8d2c6`	completed	April 28, 2026, 12:27 p.m.
NER	Named-entity recognition	`batch_69f669d1c0e481909cbbda806ac0ca28`	completed	May 2, 2026, 9:17 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a26aa556d80819099f19385aaf3beda`	completed	June 8, 2026, 11:41 a.m.
NEDg	Description generation	`batch_6a26b0a8a1ac81909a7b324f3eb9f48f`	completed	June 8, 2026, 12:08 p.m.
NED2	Entity disambiguation (via description)	`batch_6a26b13bd3c48190b37bf59f3dab95c4`	completed	June 8, 2026, 12:10 p.m.

Created at: April 28, 2026, 2:38 p.m.