Triple

T32239070
Position Surface form Disambiguated ID Type / Status
Subject Manseibashi temporary stop E823557 entity
Predicate preceded P97 FINISHED
Object Akihabara Station area
The Akihabara Station area is a bustling Tokyo district centered around Akihabara Station, famous for its dense concentration of electronics shops, anime and manga culture, and themed entertainment venues.
 E2000155 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: Akihabara Station area | Statement: [Manseibashi temporary stop, preceded, Akihabara Station area]
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: Akihabara Station area
Triple: [Manseibashi temporary stop, preceded, Akihabara Station area]
Generated description
The Akihabara Station area is a bustling Tokyo district centered around Akihabara Station, famous for its dense concentration of electronics shops, anime and manga culture, and themed entertainment venues.

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_69f3490c140481908ed53b98b561eaa1 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc00aea88190917a1ac58d2f5117 completed May 3, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46cdb7bc8190a55a42ff52f991f2 completed June 15, 2026, 12:26 a.m.
NEDg Description generation batch_6a2f79b71a308190b06f4954beb6a535 completed June 15, 2026, 4:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2f7a7d17e48190a40f7f79299f9dbe completed June 15, 2026, 4:07 a.m.
Created at: May 1, 2026, 12:39 a.m.