Triple

T12536217
Position Surface form Disambiguated ID Type / Status
Subject Kessingland E299696 entity
Predicate hasPostTown P2711 FINISHED
Object LOWESTOFT
LOWESTOFT is a coastal town in Suffolk, England, known as the easternmost settlement in the United Kingdom and a traditional seaside resort.
 E989092 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: LOWESTOFT | Statement: [Kessingland, hasPostTown, LOWESTOFT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LOWESTOFT
Context triple: [Kessingland, hasPostTown, LOWESTOFT]
  • A. How Low
    "How Low" is a popular hip-hop single by American rapper Ludacris, known for its catchy hook and heavy club-oriented production.
  • B. LOWS
    LOWS is the ICAO airport code for Salzburg Airport in Austria.
  • C. On the Low
    "On the Low" is a popular Afro-fusion song by Nigerian artist Burna Boy, known for its smooth melody, romantic lyrics, and widespread international success.
  • D. Get Low
    "Get Low" is a 2002 crunk anthem by Lil Jon & the East Side Boyz featuring the Ying Yang Twins that became a major club hit and a defining track of early-2000s Southern hip hop.
  • E. The Lowest Trees Have Tops
    The Lowest Trees Have Tops is a novel by war correspondent and author Martha Gellhorn that explores personal and political tensions in mid-20th-century Europe.
  • 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: LOWESTOFT
Triple: [Kessingland, hasPostTown, LOWESTOFT]
Generated description
LOWESTOFT is a coastal town in Suffolk, England, known as the easternmost settlement in the United Kingdom and a traditional seaside resort.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LOWESTOFT
Target entity description: LOWESTOFT is a coastal town in Suffolk, England, known as the easternmost settlement in the United Kingdom and a traditional seaside resort.
  • A. How Low
    "How Low" is a popular hip-hop single by American rapper Ludacris, known for its catchy hook and heavy club-oriented production.
  • B. LOWS
    LOWS is the ICAO airport code for Salzburg Airport in Austria.
  • C. On the Low
    "On the Low" is a popular Afro-fusion song by Nigerian artist Burna Boy, known for its smooth melody, romantic lyrics, and widespread international success.
  • D. Get Low
    "Get Low" is a 2002 crunk anthem by Lil Jon & the East Side Boyz featuring the Ying Yang Twins that became a major club hit and a defining track of early-2000s Southern hip hop.
  • E. The Lowest Trees Have Tops
    The Lowest Trees Have Tops is a novel by war correspondent and author Martha Gellhorn that explores personal and political tensions in mid-20th-century Europe.
  • 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_69d6ada707008190aaec1238117c9379 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9546d3b2081908d3e0659f8f13678 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6557a95d48190ac588807544f060f completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f6566e7bf88190a33c609caf9b4f3d completed May 2, 2026, 7:54 p.m.
NED2 Entity disambiguation (via description) batch_69f657aa1bf48190a884e0dfce31e30e completed May 2, 2026, 7:59 p.m.
Created at: April 8, 2026, 9:57 p.m.