Triple

T24761374
Position Surface form Disambiguated ID Type / Status
Subject Margaret Johnston E619452 entity
Predicate notableWork P4 FINISHED
Object The Harassed Hero
The Harassed Hero is a work by British actress and writer Margaret Johnston, likely reflecting her mid-20th-century theatrical sensibilities and character-driven storytelling.
 E1653341 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: The Harassed Hero | Statement: [Margaret Johnston, notableWork, The Harassed Hero]
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: The Harassed Hero
Triple: [Margaret Johnston, notableWork, The Harassed Hero]
Generated description
The Harassed Hero is a work by British actress and writer Margaret Johnston, likely reflecting her mid-20th-century theatrical sensibilities and character-driven storytelling.

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_69e2fabbea94819092ed41348909622f completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4107c1e088190b2262059a50d1b6b completed May 1, 2026, 2:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c1008708190a48a1c4755381f45 completed May 22, 2026, 9:04 a.m.
NEDg Description generation batch_6a1028468998819087e3f9b72b85b947 completed May 22, 2026, 9:56 a.m.
NED2 Entity disambiguation (via description) batch_6a10291de8b081908ee2e532ddca698e completed May 22, 2026, 9:59 a.m.
Created at: April 18, 2026, 4:27 a.m.