Triple

T27809689
Position Surface form Disambiguated ID Type / Status
Subject Francis Xavier Atencio E702483 entity
Predicate workedOn P3 FINISHED
Object Walt Disney World attractions
Walt Disney World attractions are the themed rides, shows, and experiences found across the resort’s multiple parks, ranging from classic dark rides and thrill coasters to immersive storytelling environments.
E1790116 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: Walt Disney World attractions | Statement: [Francis Xavier Atencio, workedOn, Walt Disney World attractions]
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: Walt Disney World attractions
Triple: [Francis Xavier Atencio, workedOn, Walt Disney World attractions]
Generated description
Walt Disney World attractions are the themed rides, shows, and experiences found across the resort’s multiple parks, ranging from classic dark rides and thrill coasters to immersive storytelling environments.

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_69ef840a16748190926719ab96120bae completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f6383da0e08190a4e394aba4b49348 completed May 2, 2026, 5:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ecd73784819084a0dfc4fe1faf1f completed May 24, 2026, 12:19 p.m.
NEDg Description generation batch_6a12f0b6e8f481908e8f9fe81762294d completed May 24, 2026, 12:36 p.m.
NED2 Entity disambiguation (via description) batch_6a12f1572b5c819092860ccde27a0662 completed May 24, 2026, 12:38 p.m.
Created at: April 27, 2026, 5:41 p.m.