Triple

T30943407
Position Surface form Disambiguated ID Type / Status
Subject UCA government E788320 entity
Predicate establishes P986 FINISHED
Object knot cities network
The knot cities network is an interconnected system of urban centers coordinated under the UCA government to function as a unified socio-economic and infrastructural whole.
E1937513 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: knot cities network | Statement: [UCA government, establishes, knot cities network]
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: knot cities network
Triple: [UCA government, establishes, knot cities network]
Generated description
The knot cities network is an interconnected system of urban centers coordinated under the UCA government to function as a unified socio-economic and infrastructural whole.

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_69f224c180f88190ad177372ee02b7e2 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69311de408190a703476e7a0557b6 completed May 3, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28e47a048881909294f100074dd7f4 completed June 10, 2026, 4:13 a.m.
NEDg Description generation batch_6a28e5a74b608190844a6367f9e177dd completed June 10, 2026, 4:18 a.m.
NED2 Entity disambiguation (via description) batch_6a28e62421448190b62bb7e8cfdf8541 completed June 10, 2026, 4:20 a.m.
Created at: April 29, 2026, 8:53 p.m.