Triple

T9810279
Position Surface form Disambiguated ID Type / Status
Subject Markus Wenzel E238249 entity
Predicate softwareProject P25429 FINISHED
Object Isabelle
Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
 E824319 NE FINISHED

How this triple was built (4 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: Isabelle | Statement: [Markus Wenzel, softwareProject, Isabelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Isabelle
Context triple: [Markus Wenzel, softwareProject, Isabelle]
  • A. Isabelle
    Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
  • B. Isabel
    Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
  • C. Isabel
    Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
  • D. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • E. Isabella
    Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Isabelle
Triple: [Markus Wenzel, softwareProject, Isabelle]
Generated description
Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Isabelle
Target entity description: Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
  • A. Isabelle
    Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
  • B. Isabel
    Isabel is a feminine given name of Spanish origin, widely used in Spanish- and Portuguese-speaking countries and borne by numerous notable historical and contemporary figures.
  • C. Isabel
    Isabel is a Spanish historical drama television series centered on the life and reign of Queen Isabella I of Castile.
  • D. Isabella
    Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
  • E. Isabella
    Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
  • F. None of above. chosen

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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb220310c8190a16ca0b746f0ef7a completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1d5b264c88190bf16c8c32c360878 completed April 5, 2026, 3:23 a.m.
NEDg Description generation batch_69d1d6252f6c819087977f0713e53e50 completed April 5, 2026, 3:25 a.m.
NED2 Entity disambiguation (via description) batch_69d1d6a4429c8190b761dea635c6674e completed April 5, 2026, 3:27 a.m.
Created at: March 30, 2026, 8:30 p.m.