Triple

T21200367
Position Surface form Disambiguated ID Type / Status
Subject Battle of Reading (871) E522437 entity
Predicate opposingCommander P1698 FINISHED
Object Bagsecg NE NERFINISHED

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: Bagsecg | Statement: [Battle of Reading (871), opposingCommander, Bagsecg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bagsecg
Context triple: [Battle of Reading (871), opposingCommander, Bagsecg]
  • A. Bagsecg chosen
    Bagsecg was a 9th-century Viking war leader and king who played a major role in the Norse invasions of Anglo-Saxon England.
  • B. BSEC
    BSEC is a regional intergovernmental organization that promotes economic cooperation and development among countries in the Black Sea region.
  • C. Seco
    Seco is Switzerland’s State Secretariat for Economic Affairs, responsible for national and international economic policy, labor market issues, and trade promotion.
  • D. SECAmb
    SECAmb is the National Health Service ambulance trust responsible for providing emergency medical services across the South East of England.
  • E. GateKeeper
    GateKeeper is a steel wing roller coaster at Cedar Point in Ohio, renowned for its record-breaking inversions and dramatic keyhole elements, designed by Swiss manufacturer Bolliger & Mabillard.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b5112d8881909510b2dcdc93106d completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e73430c5a08190aeb6a62eec0f43a3 completed April 21, 2026, 8:24 a.m.
Created at: April 16, 2026, 3:18 p.m.