Triple

T34597837
Position Surface form Disambiguated ID Type / Status
Subject Maine International Film Festival E888364 entity
Predicate hasVenue P373 FINISHED
Object Railroad Square Cinema
Railroad Square Cinema is an independent arthouse movie theater in Waterville, Maine, known for showcasing diverse, non-mainstream films and hosting major regional film events.
E2104741 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: Railroad Square Cinema | Statement: [Maine International Film Festival, hasVenue, Railroad Square 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: Railroad Square Cinema
Triple: [Maine International Film Festival, hasVenue, Railroad Square Cinema]
Generated description
Railroad Square Cinema is an independent arthouse movie theater in Waterville, Maine, known for showcasing diverse, non-mainstream films and hosting major regional film events.

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_69f349d3bfcc81909874c99e646fb3ea completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f72161da1081909c1f834398777256 completed May 3, 2026, 10:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37410a2d5881909723be3435823e4a completed June 21, 2026, 1:40 a.m.
NEDg Description generation batch_6a374208897081909434c3a2e34d2d2f completed June 21, 2026, 1:44 a.m.
NED2 Entity disambiguation (via description) batch_6a374329be488190bffd50363cf3b8f0 completed June 21, 2026, 1:49 a.m.
Created at: May 1, 2026, 2:03 a.m.