Triple

T7456777
Position Surface form Disambiguated ID Type / Status
Subject Old Uyghur alphabet E172144 entity
Predicate influenced P9 FINISHED
Object Clear Script (Todo) E300217 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: Clear Script (Todo) | Statement: [Old Uyghur alphabet, influenced, Clear Script (Todo)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Clear Script (Todo)
Context triple: [Old Uyghur alphabet, influenced, Clear Script (Todo)]
  • A. Clear script (Todo script) chosen
    Clear script (Todo script) is a vertically written alphabetic script developed in the 17th century for writing Mongolic languages, particularly among the Oirat Mongols.
  • B. Interface Clear
    Interface Clear is a company specializing in building automation and control systems, particularly focused on integrating and managing HVAC and related building technologies.
  • C. St Clears
    St Clears is a small town and community in Carmarthenshire, Wales, known for its historic abbey remains and position near the River Taf.
  • D. Clearing
    Clearing is a residential and industrial neighborhood on the southwest side of Chicago, known for encompassing and surrounding Midway International Airport.
  • E. Clearing
    "Clearing" is a large-scale photographic work by German artist Thomas Demand, known for its meticulously constructed paper model of a forest scene that blurs the line between reality and fabrication.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c68a66554c8190add75c65942c0317 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f3b0780881909e645160dd49eb57 completed March 27, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c827c39a848190bc275468362ce3bc completed March 28, 2026, 7:10 p.m.
Created at: March 27, 2026, 3:15 p.m.