Triple

T37595933
Position Surface form Disambiguated ID Type / Status
Subject Beverly Hills Post Office area E935385 entity
Predicate adjacentTo P224 FINISHED
Object Beverly Hills
Beverly Hills is an affluent, internationally famous city in Los Angeles County known for its luxury shopping on Rodeo Drive, celebrity residents, and iconic palm-lined streets.
E164932 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: Beverly Hills | Statement: [Beverly Hills Post Office area, adjacentTo, Beverly Hills]
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: Beverly Hills
Triple: [Beverly Hills Post Office area, adjacentTo, Beverly Hills]
Generated description
Beverly Hills is an affluent, internationally famous city in Los Angeles County known for its luxury shopping on Rodeo Drive, celebrity residents, and iconic palm-lined streets.

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_69f76ecf39c081909baffe597bb55273 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba8c1c7888190bb5f3108ba1cbbfe completed May 6, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40a802c9cc8190a82c53bc8095a477 completed June 28, 2026, 4:50 a.m.
NEDg Description generation batch_6a40a93f15e481909584730b9d6a8ca1 completed June 28, 2026, 4:55 a.m.
NED2 Entity disambiguation (via description) batch_6a40a9c509e48190803e29dc13c1ce8d completed June 28, 2026, 4:57 a.m.
Created at: May 3, 2026, 4:18 p.m.