Triple

T36506738
Position Surface form Disambiguated ID Type / Status
Subject Helen Luella Koford E899484 entity
Predicate notableWork P4 FINISHED
Object King of the Khyber Rifles
King of the Khyber Rifles is a 1953 Technicolor adventure film set on the Northwest Frontier of British India, starring Tyrone Power and Terry Moore in a story of military intrigue and romance.
E2187962 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: King of the Khyber Rifles | Statement: [Helen Luella Koford, notableWork, King of the Khyber Rifles]
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: King of the Khyber Rifles
Triple: [Helen Luella Koford, notableWork, King of the Khyber Rifles]
Generated description
King of the Khyber Rifles is a 1953 Technicolor adventure film set on the Northwest Frontier of British India, starring Tyrone Power and Terry Moore in a story of military intrigue and romance.

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_69f76e5b92088190933afda3f7531dd4 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c1c731508190b45000bf01654e6e completed May 3, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39dbd567448190b9e3e3aa8ec774c5 completed June 23, 2026, 1:05 a.m.
NEDg Description generation batch_6a39dd5a787c819089c278f86de8078c completed June 23, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_6a39e17cb03c8190830fe006a9dd4455 completed June 23, 2026, 1:29 a.m.
Created at: May 3, 2026, 4:10 p.m.