Triple

T2993338
Position Surface form Disambiguated ID Type / Status
Subject MKE Ankaragücü E81006 entity
Predicate badgeText P44573 FINISHED
Object MKE
MKE is the abbreviated name commonly associated with the Turkish sports club MKE Ankaragücü.
 E317495 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: MKE | Statement: [MKE Ankaragücü, badgeText, MKE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MKE
Context triple: [MKE Ankaragücü, badgeText, MKE]
  • A. Milwaukee
    Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
  • B. Milwaukie
    Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
  • C. Orel
    Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
  • D. Queen City
    Queen City is a popular nickname for Cincinnati, Ohio, highlighting its historic prominence and cultural importance in the region.
  • E. Queen City
    Queen City is the popular nickname for Charlotte, North Carolina, a major financial and cultural hub in the southeastern United States.
  • 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: MKE
Triple: [MKE Ankaragücü, badgeText, MKE]
Generated description
MKE is the abbreviated name commonly associated with the Turkish sports club MKE Ankaragücü.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MKE
Target entity description: MKE is the abbreviated name commonly associated with the Turkish sports club MKE Ankaragücü.
  • A. Milwaukee
    Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
  • B. Milwaukie
    Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
  • C. Orel
    Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
  • D. Queen City
    Queen City is a popular nickname for Cincinnati, Ohio, highlighting its historic prominence and cultural importance in the region.
  • E. Queen City
    Queen City is the popular nickname for Charlotte, North Carolina, a major financial and cultural hub in the southeastern United States.
  • 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_69ad8b187fc8819085914d3c9ea3142d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9e11c4188190a3ae8fd0cbd8c2c0 completed March 8, 2026, 4:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69b109061684819086777d3b871c94f8 completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b1196399e881908887513a3bdf7f98 completed March 11, 2026, 7:27 a.m.
NED2 Entity disambiguation (via description) batch_69b119d306208190b53b059f0ff57712 completed March 11, 2026, 7:29 a.m.
Created at: March 8, 2026, 2:59 p.m.