Triple

T32857731
Position Surface form Disambiguated ID Type / Status
Subject Kidcity Children’s Museum E840423 entity
Predicate hasExhibit P35 FINISHED
Object Main Street exhibit
The Main Street exhibit is a child-sized, interactive play area at Kidcity Children’s Museum that lets kids explore a pretend town with shops, services, and everyday community spaces.
 E2026927 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: Main Street exhibit | Statement: [Kidcity Children’s Museum, hasExhibit, Main Street exhibit]
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: Main Street exhibit
Triple: [Kidcity Children’s Museum, hasExhibit, Main Street exhibit]
Generated description
The Main Street exhibit is a child-sized, interactive play area at Kidcity Children’s Museum that lets kids explore a pretend town with shops, services, and everyday community spaces.

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_69f34942465c819099b3fb47f9044f58 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6ceb2b81c81909c1a186305a9d597 completed May 3, 2026, 4:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bd0d31548190a9f254e4e5577320 completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34c10264a88190b314ef6eb9dab963 completed June 19, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_6a34c13e30948190b22b7a3dd3b2909a completed June 19, 2026, 4:10 a.m.
Created at: May 1, 2026, 1:17 a.m.