Triple

T6584041
Position Surface form Disambiguated ID Type / Status
Subject Teddy Kollek E159175 entity
Predicate nickname P55 FINISHED
Object Teddy
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
 E603960 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: Teddy | Statement: [Teddy Kollek, nickname, Teddy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teddy
Context triple: [Teddy Kollek, nickname, Teddy]
  • A. Teddy
    Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
  • B. Teddy
    Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
  • C. Teddy
    Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
  • D. Ted (living teddy bear)
    Ted (living teddy bear) is a foul-mouthed, magically animated stuffed bear who serves as the crude yet lovable best friend of John Bennett in the comedic Ted film franchise.
  • E. Teddy Bears
    Teddy Bears is a popular nickname for Rangers F.C., one of Scotland’s most successful and widely supported football clubs.
  • 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: Teddy
Triple: [Teddy Kollek, nickname, Teddy]
Generated description
Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teddy
Target entity description: Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
  • A. Teddy
    Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
  • B. Teddy
    Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
  • C. Teddy
    Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
  • D. Ted (living teddy bear)
    Ted (living teddy bear) is a foul-mouthed, magically animated stuffed bear who serves as the crude yet lovable best friend of John Bennett in the comedic Ted film franchise.
  • E. Teddy Bears
    Teddy Bears is a popular nickname for Rangers F.C., one of Scotland’s most successful and widely supported football clubs.
  • 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_69c688366ce8819083f8883983c0df92 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae938184819088234aad9cc997e1 completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d576ab288190ac7e7b58e974697c completed March 27, 2026, 7:07 p.m.
NEDg Description generation batch_69c6d828620081909c1b4dfaa96efd62 completed March 27, 2026, 7:19 p.m.
NED2 Entity disambiguation (via description) batch_69c6d8a3d194819080f33e179a8a8679 completed March 27, 2026, 7:21 p.m.
Created at: March 27, 2026, 1:54 p.m.