Triple

T36371582
Position Surface form Disambiguated ID Type / Status
Subject Lanark E895769 entity
Predicate setting P1957 FINISHED
Object fictional city of Unthank
The fictional city of Unthank is a dark, surreal urban landscape central to Alasdair Gray’s novel "Lanark," symbolizing alienation, bureaucracy, and dystopian modern life.
E2180490 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: fictional city of Unthank | Statement: [Lanark, setting, fictional city of Unthank]
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: fictional city of Unthank
Triple: [Lanark, setting, fictional city of Unthank]
Generated description
The fictional city of Unthank is a dark, surreal urban landscape central to Alasdair Gray’s novel "Lanark," symbolizing alienation, bureaucracy, and dystopian modern life.

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_69f76e5115588190ad8738860b7bc68b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7baf108a8819089e231babc53f8f7 completed May 3, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39a337c3848190be56b96870d4a13d completed June 22, 2026, 9:03 p.m.
NEDg Description generation batch_6a39a3e4a9488190a77ea18c86b5af48 completed June 22, 2026, 9:06 p.m.
NED2 Entity disambiguation (via description) batch_6a39a49278088190b10c591fec7c42e2 completed June 22, 2026, 9:09 p.m.
Created at: May 3, 2026, 4:10 p.m.