Triple

T34665235
Position Surface form Disambiguated ID Type / Status
Subject Catch E890232 entity
Predicate hasSisterRestaurant P31639 FINISHED
Object Catch LA
Catch LA is a trendy, celebrity-frequented seafood and sushi restaurant and rooftop hotspot in West Hollywood known for its upscale ambiance and Instagrammable decor.
 E2106715 NE FINISHED

How this triple was built (3 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: Catch LA | Statement: [Catch, hasSisterRestaurant, Catch LA]
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: Catch LA
Triple: [Catch, hasSisterRestaurant, Catch LA]
Generated description
Catch LA is a trendy, celebrity-frequented seafood and sushi restaurant and rooftop hotspot in West Hollywood known for its upscale ambiance and Instagrammable decor.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasSisterRestaurant
Context triple: [Catch, hasSisterRestaurant, Catch LA]
  • A. rivalRestaurantOf
    Indicates that one restaurant competes with another restaurant in the same market or customer base.
  • B. hasSisterResort
    Indicates that one resort is formally associated with another as its sister property, typically under common ownership or branding.
  • C. hasSister chosen
    Indicates that one entity is the sister of another entity.
  • D. hasSiblingFacility
    Indicates that one facility is related to another as a sibling facility, typically sharing a common parent organization or similar hierarchical level.
  • E. sisterVenue
    Indicates that two venues are related as peers or counterparts, typically sharing ownership, branding, or affiliation without one being subordinate to the other.
  • F. None of above.

Provenance (6 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_69f349d9c59481908b36baa0be093aea completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69fd8ccbd4c88190b13aae0673b3c821 completed May 8, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a374903aa648190b9beaf59b4562d53 completed June 21, 2026, 2:14 a.m.
NEDg Description generation batch_6a374a91d4f08190bc2df424a4136b3d completed June 21, 2026, 2:21 a.m.
NED2 Entity disambiguation (via description) batch_6a374b44cba88190999dede2bc2a408e completed June 21, 2026, 2:24 a.m.
PD Predicate disambiguation batch_69fd8ae2227c819089546f5c3629799e completed May 8, 2026, 7:04 a.m.
Created at: May 1, 2026, 2:04 a.m.