Triple

T3000882
Position Surface form Disambiguated ID Type / Status
Subject Mount Kenya E81182 entity
Predicate hasSummit P8024 FINISHED
Object Point Lenana
Point Lenana is the third-highest peak of Mount Kenya and a popular, non-technical trekking summit for climbers.
 E318862 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: Point Lenana | Statement: [Mount Kenya, hasSummit, Point Lenana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Point Lenana
Context triple: [Mount Kenya, hasSummit, Point Lenana]
  • A. Courthézon
    Courthézon is a commune in southeastern France’s Vaucluse department, known for its historic village center, wine production in the Côtes du Rhône region, and proximity to Châteauneuf-du-Pape.
  • B. L’Espoir
    L’Espoir is a 1937 novel by André Malraux that portrays the political and human drama of the Spanish Civil War.
  • C. Nelson
    Nelson is a former mill town in Lancashire, England, known for its industrial heritage and location near the Pennine hills.
  • D. Nelson
    Nelson is a coastal city at the top of New Zealand’s South Island, known for its arts scene, sunny climate, and access to nearby national parks.
  • E. Nelson
    Nelson is a common English-language surname borne by numerous notable figures across politics, sports, entertainment, and academia.
  • 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: Point Lenana
Triple: [Mount Kenya, hasSummit, Point Lenana]
Generated description
Point Lenana is the third-highest peak of Mount Kenya and a popular, non-technical trekking summit for climbers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Point Lenana
Target entity description: Point Lenana is the third-highest peak of Mount Kenya and a popular, non-technical trekking summit for climbers.
  • A. Courthézon
    Courthézon is a commune in southeastern France’s Vaucluse department, known for its historic village center, wine production in the Côtes du Rhône region, and proximity to Châteauneuf-du-Pape.
  • B. L’Espoir
    L’Espoir is a 1937 novel by André Malraux that portrays the political and human drama of the Spanish Civil War.
  • C. Nelson
    Nelson is a former mill town in Lancashire, England, known for its industrial heritage and location near the Pennine hills.
  • D. Nelson
    Nelson is a coastal city at the top of New Zealand’s South Island, known for its arts scene, sunny climate, and access to nearby national parks.
  • E. Nelson
    Nelson is a masculine given name of English origin that has been borne by various notable figures in politics, sports, and the arts.
  • 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_69ad8b187fc8819085914d3c9ea3142d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a1022e48190afee77db94635ff2 completed March 8, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b12e4b54188190bf900bf10061a57a completed March 11, 2026, 8:56 a.m.
NEDg Description generation batch_69b12f188c7c81908d1d575252dc4bda completed March 11, 2026, 9 a.m.
NED2 Entity disambiguation (via description) batch_69b1c9bccb3081909e6869b5cba68117 completed March 11, 2026, 7:59 p.m.
Created at: March 8, 2026, 2:59 p.m.