Triple

T32489167
Position Surface form Disambiguated ID Type / Status
Subject Meguro City Office E830331 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Meguro City
Meguro City is a special ward in Tokyo, Japan, known for its residential neighborhoods, trendy cafes, and cultural attractions along the Meguro River.
E2162182 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: Meguro City | Statement: [Meguro City Office, locatedInAdministrativeTerritory, Meguro City]
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: Meguro City
Triple: [Meguro City Office, locatedInAdministrativeTerritory, Meguro City]
Generated description
Meguro City is a special ward in Tokyo, Japan, known for its residential neighborhoods, trendy cafes, and cultural attractions along the Meguro River.

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_69f34920aa4081908d8fb0277414b911 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c3faab78819095c5bfa180e15186 completed May 3, 2026, 3:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a38ae0622548190acbb9c4b674e4798 completed June 22, 2026, 3:37 a.m.
NEDg Description generation batch_6a38b06544c481909b80df630265c8b8 completed June 22, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_6a38b0f5d24881909014f9d2539b0138 completed June 22, 2026, 3:50 a.m.
Created at: May 1, 2026, 12:59 a.m.