Triple

T35159779
Position Surface form Disambiguated ID Type / Status
Subject Philip Toynbee E1015231 entity
Predicate notableWork P4 FINISHED
Object A Learned City
"A Learned City" is a work by British writer and critic Philip Toynbee, reflecting his characteristic intellectual and literary concerns.
 E2128325 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 Learned City | Statement: [Philip Toynbee, notableWork, A Learned City]
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 Learned City
Triple: [Philip Toynbee, notableWork, A Learned City]
Generated description
"A Learned City" is a work by British writer and critic Philip Toynbee, reflecting his characteristic intellectual and literary concerns.

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_69f76ddb3a708190b521ba2970b17178 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78d2a34fc81909525f52635952ea2 completed May 3, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37d96864cc81909ba8531d98805f58 completed June 21, 2026, 12:30 p.m.
NEDg Description generation batch_6a37dc5190d481908a8128de9d4f5ee5 completed June 21, 2026, 12:42 p.m.
NED2 Entity disambiguation (via description) batch_6a37dd1e759081909e2244af9ee715e6 completed June 21, 2026, 12:46 p.m.
Created at: May 3, 2026, 4:02 p.m.