Triple

T33896010
Position Surface form Disambiguated ID Type / Status
Subject Göran Therborn E868911 entity
Predicate notableWork P4 FINISHED
Object Cities of Power
Cities of Power is a scholarly book by sociologist Göran Therborn that analyzes how major cities shape and reflect global power structures, politics, and social inequalities.
 E2073481 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: Cities of Power | Statement: [Göran Therborn, notableWork, Cities of Power]
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: Cities of Power
Triple: [Göran Therborn, notableWork, Cities of Power]
Generated description
Cities of Power is a scholarly book by sociologist Göran Therborn that analyzes how major cities shape and reflect global power structures, politics, and social inequalities.

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_69f34997703c8190866b1d404bce531f completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7017f668c81909728d5db01e77d10 completed May 3, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36823e736481908aef1d6187062cf3 completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a3683b5ac0881909f03e1e94896067d completed June 20, 2026, 12:12 p.m.
NED2 Entity disambiguation (via description) batch_6a36848248c081909b5ab57a8c3accc6 completed June 20, 2026, 12:16 p.m.
Created at: May 1, 2026, 1:48 a.m.