Triple

T28297690
Position Surface form Disambiguated ID Type / Status
Subject Veck Sims E713614 entity
Predicate employerInFilm P40735 FINISHED
Object West Orange Pavilion Mall security
West Orange Pavilion Mall security is the fictional mall security department featured in the film where the character Veck Sims is employed as a guard.
 E1810157 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: West Orange Pavilion Mall security | Statement: [Veck Sims, employerInFilm, West Orange Pavilion Mall security]
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: West Orange Pavilion Mall security
Triple: [Veck Sims, employerInFilm, West Orange Pavilion Mall security]
Generated description
West Orange Pavilion Mall security is the fictional mall security department featured in the film where the character Veck Sims is employed as a guard.

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_69efb524ab688190a1ce7ee7c9520932 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644b0048c8190a4fb9ea056b8c811 completed May 2, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1607333edc8190881370b43a6014f8 completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a1610d76f348190942a9ed07eaddb50 completed May 26, 2026, 9:29 p.m.
NED2 Entity disambiguation (via description) batch_6a16111dc4dc81909716b680152047c7 completed May 26, 2026, 9:31 p.m.
Created at: April 27, 2026, 11:33 p.m.