Triple

T2699248
Position Surface form Disambiguated ID Type / Status
Subject Central District E58589 entity
Predicate hasLandmark P105 FINISHED
Object PMQ
PMQ is a revitalized creative hub and design center in Hong Kong’s Central District, housed in the former Police Married Quarters and now home to studios, shops, and cultural events.
E291442 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: PMQ | Statement: [Central District, hasLandmark, PMQ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PMQ
Context triple: [Central District, hasLandmark, PMQ]
  • A. MQ
    MQ is IBM's messaging middleware that enables reliable, asynchronous communication between distributed applications and systems.
  • B. MQ
    MQ is the IATA airline designator used by American Eagle Airlines for its flight operations.
  • C. MQ
    MQ is a German vehicle registration code assigned to the Saalekreis district in the state of Saxony-Anhalt.
  • D. PM
    PM is the commonly used abbreviation for the Prime Minister of India, the head of the Indian government.
  • E. PM
    PM is the international vehicle registration code assigned to the French overseas collectivity of Saint Pierre and Miquelon.
  • 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: PMQ
Triple: [Central District, hasLandmark, PMQ]
Generated description
PMQ is a revitalized creative hub and design center in Hong Kong’s Central District, housed in the former Police Married Quarters and now home to studios, shops, and cultural events.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: PMQ
Target entity description: PMQ is a revitalized creative hub and design center in Hong Kong’s Central District, housed in the former Police Married Quarters and now home to studios, shops, and cultural events.
  • A. MQ
    MQ is IBM's messaging middleware that enables reliable, asynchronous communication between distributed applications and systems.
  • B. MQ
    MQ is the IATA airline designator used by American Eagle Airlines for its flight operations.
  • C. MQ
    MQ is a German vehicle registration code assigned to the Saalekreis district in the state of Saxony-Anhalt.
  • D. PM
    PM is the commonly used abbreviation for the Prime Minister of India, the head of the Indian government.
  • E. PM
    PM is the international vehicle registration code assigned to the French overseas collectivity of Saint Pierre and Miquelon.
  • 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_69ab4ac269e481909cb317d79e68b75b completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abda339cf48190b9ae6b99137f005e completed March 7, 2026, 7:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf70f9088190acd4c8bd05deea47 completed March 10, 2026, 5:43 a.m.
NEDg Description generation batch_69afb045e304819080efecf2738453e6 completed March 10, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69afb0ab2a8081909e729f883a7438d7 completed March 10, 2026, 5:48 a.m.
Created at: March 6, 2026, 9:55 p.m.