Triple

T32265679
Position Surface form Disambiguated ID Type / Status
Subject The Centurion Lounge E824275 entity
Predicate competitor P1375 FINISHED
Object Priority Pass lounges
Priority Pass lounges are a global network of airport lounges offering travelers access to comfortable seating, refreshments, and amenities regardless of their airline or ticket class.
 E2000044 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: Priority Pass lounges | Statement: [The Centurion Lounge, competitor, Priority Pass lounges]
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: Priority Pass lounges
Triple: [The Centurion Lounge, competitor, Priority Pass lounges]
Generated description
Priority Pass lounges are a global network of airport lounges offering travelers access to comfortable seating, refreshments, and amenities regardless of their airline or ticket class.

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_69f3490e73588190915f282edd105772 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc7f0ef081909966c28aafcb9bc3 completed May 3, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46dd86fc819085caf60913a730e9 completed June 15, 2026, 12:27 a.m.
NEDg Description generation batch_6a2f77c78ab481908ad9e29790e6cd6b completed June 15, 2026, 3:55 a.m.
NED2 Entity disambiguation (via description) batch_6a2f7864c3808190a0f619d122f27d37 completed June 15, 2026, 3:58 a.m.
Created at: May 1, 2026, 12:42 a.m.