Triple

T9680287
Position Surface form Disambiguated ID Type / Status
Subject Southern Alberta E234261 entity
Predicate hasCity P316 FINISHED
Object Taber
Taber is a small town in southern Alberta, Canada, best known for its agriculture, particularly its high-quality corn production.
 E813426 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: Taber | Statement: [Southern Alberta, hasCity, Taber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Taber
Context triple: [Southern Alberta, hasCity, Taber]
  • A. Tabot
    Tabot is a consecrated replica of the Ark of the Covenant central to Ethiopian Orthodox worship and the celebration of the Divine Liturgy.
  • B. Tubbs
    Tubbs is the surname of Rallo Tubbs, a young animated character from the television series "The Cleveland Show."
  • C. Collip
    Collip is a surname most notably associated with James Collip, a Canadian biochemist who was part of the team that developed insulin as a treatment for diabetes.
  • D. Tawney
    Tawney is an English surname most notably associated with the influential economic historian and social critic R. H. Tawney.
  • E. Taube
    Taube is a surname most notably associated with Henry Taube, a Canadian-American chemist and Nobel laureate recognized for his work on the mechanisms of electron-transfer reactions in metal complexes.
  • 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: Taber
Triple: [Southern Alberta, hasCity, Taber]
Generated description
Taber is a small town in southern Alberta, Canada, best known for its agriculture, particularly its high-quality corn production.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Taber
Target entity description: Taber is a small town in southern Alberta, Canada, best known for its agriculture, particularly its high-quality corn production.
  • A. Tabot
    Tabot is a consecrated replica of the Ark of the Covenant central to Ethiopian Orthodox worship and the celebration of the Divine Liturgy.
  • B. Tubbs
    Tubbs is the surname of Rallo Tubbs, a young animated character from the television series "The Cleveland Show."
  • C. Collip
    Collip is a surname most notably associated with James Collip, a Canadian biochemist who was part of the team that developed insulin as a treatment for diabetes.
  • D. Tawney
    Tawney is an English surname most notably associated with the influential economic historian and social critic R. H. Tawney.
  • E. Taube
    Taube is a surname most notably associated with Henry Taube, a Canadian-American chemist and Nobel laureate recognized for his work on the mechanisms of electron-transfer reactions in metal complexes.
  • 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_69ca84c99e34819092e5563a7106cfca completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9c9dcbe881908ae926a5b5eae759 completed April 1, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a395a508190a185d08f3719ee09 completed April 4, 2026, 10:01 p.m.
NEDg Description generation batch_69d18b28b8c88190ba658f7c3f5e438c completed April 4, 2026, 10:05 p.m.
NED2 Entity disambiguation (via description) batch_69d18bb5d1908190a34dec67fd98a226 completed April 4, 2026, 10:07 p.m.
Created at: March 30, 2026, 8:16 p.m.