Triple

T35891947
Position Surface form Disambiguated ID Type / Status
Subject Georgia King E1038105 entity
Predicate notableWork P4 FINISHED
Object One Night in Emergency
One Night in Emergency is a Scottish television drama film that reimagines Homer’s Odyssey over the course of a surreal night in a modern hospital.
 E2160530 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: One Night in Emergency | Statement: [Georgia King, notableWork, One Night in Emergency]
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: One Night in Emergency
Triple: [Georgia King, notableWork, One Night in Emergency]
Generated description
One Night in Emergency is a Scottish television drama film that reimagines Homer’s Odyssey over the course of a surreal night in a modern hospital.

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_69f76e2190f88190beb2eed798a4ef01 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aa3b4914819098a40400cf7f5b31 completed May 3, 2026, 8:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4fc7efc8190900d279c9f5cfaff completed June 22, 2026, 2:59 a.m.
NEDg Description generation batch_6a38a5abc604819084de021a2c2262a3 completed June 22, 2026, 3:02 a.m.
NED2 Entity disambiguation (via description) batch_6a38a63caecc8190a4ac4eb8af4bb18b completed June 22, 2026, 3:04 a.m.
Created at: May 3, 2026, 4:06 p.m.