Triple

T613002
Position Surface form Disambiguated ID Type / Status
Subject HMS Sikh E12140 entity
Predicate pennantNumber P3153 FINISHED
Object F82
F82 is the pennant number assigned to HMS Sikh, a British Royal Navy Tribal-class destroyer that served during the Second World War.
 E76553 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: F82 | Statement: [HMS Sikh, pennantNumber, F82]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: F82
Context triple: [HMS Sikh, pennantNumber, F82]
  • A. N84
    N84 is a regional road that serves as a key junction route connecting to the town of Bastogne in Belgium.
  • B. G.826x
    G.826x is an ITU-T standards series that defines methods and requirements for packet-based frequency and time synchronization in telecommunications networks.
  • C. A682
    The A682 is a primary road in northern England that runs through Lancashire and North Yorkshire, linking towns such as Rawtenstall and serving as a key route across the Pennines.
  • D. 18F
    18F is a digital services agency within the U.S. federal government that helps agencies design, build, and improve public-facing technology and services.
  • E. FÜ
    FÜ is the vehicle registration code used on license plates for the city of Fürth in Bavaria, Germany.
  • 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: F82
Triple: [HMS Sikh, pennantNumber, F82]
Generated description
F82 is the pennant number assigned to HMS Sikh, a British Royal Navy Tribal-class destroyer that served during the Second World War.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: F82
Target entity description: F82 is the pennant number assigned to HMS Sikh, a British Royal Navy Tribal-class destroyer that served during the Second World War.
  • A. N84
    N84 is a regional road that serves as a key junction route connecting to the town of Bastogne in Belgium.
  • B. G.826x
    G.826x is an ITU-T standards series that defines methods and requirements for packet-based frequency and time synchronization in telecommunications networks.
  • C. A682
    The A682 is a primary road in northern England that runs through Lancashire and North Yorkshire, linking towns such as Rawtenstall and serving as a key route across the Pennines.
  • D. 18F
    18F is a digital services agency within the U.S. federal government that helps agencies design, build, and improve public-facing technology and services.
  • E. FÜ
    FÜ is the vehicle registration code used on license plates for the city of Fürth in Bavaria, Germany.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49e08dbf88190ab050078a63e266b completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a533dabe288190ab25bd6d76e79d06 completed March 2, 2026, 6:53 a.m.
NEDg Description generation batch_69a54e4849f48190868d7b624e450dc3 completed March 2, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_69a55048e2ec81908d306f44b2ca24fa completed March 2, 2026, 8:54 a.m.
Created at: March 1, 2026, 7:35 p.m.