Triple

T24680334
Position Surface form Disambiguated ID Type / Status
Subject Essex Crossing E611109 entity
Predicate hasPart P35 FINISHED
Object Regal Essex Crossing cinema
Regal Essex Crossing cinema is a modern movie theater complex located within Manhattan’s Essex Crossing development, offering multiple screens and contemporary amenities for filmgoers.
E1646414 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: Regal Essex Crossing cinema | Statement: [Essex Crossing, hasPart, Regal Essex Crossing cinema]
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: Regal Essex Crossing cinema
Triple: [Essex Crossing, hasPart, Regal Essex Crossing cinema]
Generated description
Regal Essex Crossing cinema is a modern movie theater complex located within Manhattan’s Essex Crossing development, offering multiple screens and contemporary amenities for filmgoers.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fbf68d48190b92e809a8947d60e completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100ffa74808190a000df2e92e438fc completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136992b481909ee04d5c09867f21 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 3:08 a.m.