Triple

T31640071
Position Surface form Disambiguated ID Type / Status
Subject Littleton, Massachusetts E807422 entity
Predicate contains P35 FINISHED
Object O’Neil Cinemas at The Point
O’Neil Cinemas at The Point is a modern movie theater complex located within The Point lifestyle and shopping center in Littleton, Massachusetts.
 E1971431 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: O’Neil Cinemas at The Point | Statement: [Littleton, Massachusetts, contains, O’Neil Cinemas at The Point]
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: O’Neil Cinemas at The Point
Triple: [Littleton, Massachusetts, contains, O’Neil Cinemas at The Point]
Generated description
O’Neil Cinemas at The Point is a modern movie theater complex located within The Point lifestyle and shopping center in Littleton, Massachusetts.

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_69f348d9ce58819093ea2da83cbeeec1 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a91a49548190a9517bc6757b7779 completed May 3, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b79daba188190b3666e4e4f1d2fb5 completed June 12, 2026, 3:15 a.m.
NEDg Description generation batch_6a2b7c00f3f481908374741f61c6e10c completed June 12, 2026, 3:24 a.m.
NED2 Entity disambiguation (via description) batch_6a2b7cc8af648190bc6c0b9472a89846 completed June 12, 2026, 3:28 a.m.
Created at: April 30, 2026, 10:49 p.m.