Triple

T28303556
Position Surface form Disambiguated ID Type / Status
Subject Frances Mayes E713770 entity
Predicate notableWork P4 FINISHED
Object A Year in the World
A Year in the World is a travel memoir by Frances Mayes that chronicles her explorations of various countries through richly detailed reflections on culture, food, and place.
 E1810808 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: A Year in the World | Statement: [Frances Mayes, notableWork, A Year in the World]
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: A Year in the World
Triple: [Frances Mayes, notableWork, A Year in the World]
Generated description
A Year in the World is a travel memoir by Frances Mayes that chronicles her explorations of various countries through richly detailed reflections on culture, food, and place.

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_69efb524ab688190a1ce7ee7c9520932 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644b4a73c81908a3e56455066d3c8 completed May 2, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a160736cf4c81909bf19d282de2194b completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a1612bdafc081908b0cd7cb342156f7 completed May 26, 2026, 9:38 p.m.
NED2 Entity disambiguation (via description) batch_6a161367f06c8190b6c19f1f3f728f00 completed May 26, 2026, 9:40 p.m.
Created at: April 27, 2026, 11:36 p.m.