Triple

T15354885
Position Surface form Disambiguated ID Type / Status
Subject Ronald McDonald E367145 entity
Predicate associatedWithCharacter P1481 FINISHED
Object Hamburglar
Hamburglar is a classic McDonaldland villain character known for his striped outfit, wide-brimmed hat, and comical attempts to steal hamburgers.
 E1151620 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: Hamburglar | Statement: [Ronald McDonald, associatedWithCharacter, Hamburglar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hamburglar
Context triple: [Ronald McDonald, associatedWithCharacter, Hamburglar]
  • A. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • B. Muscat de Hambourg
    Muscat de Hambourg is a dark-skinned Muscat grape variety primarily used to produce aromatic, sweet red and rosé wines.
  • C. Hamburg mark
    The Hamburg mark was the historical monetary unit used by the Free City of Hamburg before the adoption of the German mark.
  • D. Gotenhafen
    Gotenhafen was the German name for the port city of Gdynia in occupied Poland during World War II, used as a major naval base by the Kriegsmarine.
  • E. Bremen
    Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
  • 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: Hamburglar
Triple: [Ronald McDonald, associatedWithCharacter, Hamburglar]
Generated description
Hamburglar is a classic McDonaldland villain character known for his striped outfit, wide-brimmed hat, and comical attempts to steal hamburgers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hamburglar
Target entity description: Hamburglar is a classic McDonaldland villain character known for his striped outfit, wide-brimmed hat, and comical attempts to steal hamburgers.
  • A. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • B. Muscat de Hambourg
    Muscat de Hambourg is a dark-skinned Muscat grape variety primarily used to produce aromatic, sweet red and rosé wines.
  • C. Hamburg mark
    The Hamburg mark was the historical monetary unit used by the Free City of Hamburg before the adoption of the German mark.
  • D. Gotenhafen
    Gotenhafen was the German name for the port city of Gdynia in occupied Poland during World War II, used as a major naval base by the Kriegsmarine.
  • E. Bremen
    Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
  • 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e2a8e88819093e4b7479b2c80cd completed April 16, 2026, 1:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01ff42d48190897e6653d2b4f8a4 completed May 9, 2026, 9:44 a.m.
NEDg Description generation batch_69ff036cf4c481909eaab72ee97fbf6c completed May 9, 2026, 9:50 a.m.
NED2 Entity disambiguation (via description) batch_69ff03d829d48190990412a84f4376dc completed May 9, 2026, 9:52 a.m.
Created at: April 10, 2026, 3:18 a.m.