Triple

T3108317
Position Surface form Disambiguated ID Type / Status
Subject William V, Prince of Orange E64888 entity
Predicate deathPlace P21 FINISHED
Object Braunfels E239890 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: Braunfels | Statement: [William V, Prince of Orange, deathPlace, Braunfels]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Braunfels
Context triple: [William V, Prince of Orange, deathPlace, Braunfels]
  • A. Braunfels chosen
    Braunfels is a historic spa and castle town in the German state of Hesse, known for its well-preserved medieval old town and hilltop Braunfels Castle.
  • B. New Braunfels
    New Braunfels is a historic central Texas city known for its German heritage, river recreation on the Comal and Guadalupe, and location between Austin and San Antonio.
  • C. Corsicana
    Corsicana is a small city in north-central Texas known for its oil boom history and as a regional commercial and transportation hub between Dallas and Houston.
  • D. Balch Springs
    Balch Springs is a suburban city in the Dallas–Fort Worth metropolitan area in northeastern Texas.
  • E. Wimberley
    Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada29eacc88190a19c5ca8e53e3dca completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b324e2f1448190be5b744abdeed314 completed March 12, 2026, 8:41 p.m.
Created at: March 8, 2026, 3:04 p.m.