Triple

T13145577
Position Surface form Disambiguated ID Type / Status
Subject Eastern Highlands Province E312326 entity
Predicate hasTown P847 FINISHED
Object Okapa
Okapa is a rural town in Papua New Guinea known for its highland culture and production of premium coffee.
 E1024274 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: Okapa | Statement: [Eastern Highlands Province, hasTown, Okapa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Okapa
Context triple: [Eastern Highlands Province, hasTown, Okapa]
  • A. Kabuna
    Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
  • B. Kuno
    Kuno is the rebellious central character in E.M. Forster’s dystopian science fiction story "The Machine Stops," who challenges the oppressive, technology-dependent society in which he lives.
  • C. Kuno
    Kuno is a masculine given name of German origin, historically borne by various nobles and notable figures in German-speaking regions.
  • D. Zuchu
    Zuchu is a Tanzanian singer and songwriter known for her Bongo Flava and Afro-pop hits under the WCB Wasafi label.
  • E. Komo
    The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
  • 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: Okapa
Triple: [Eastern Highlands Province, hasTown, Okapa]
Generated description
Okapa is a rural town in Papua New Guinea known for its highland culture and production of premium coffee.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Okapa
Target entity description: Okapa is a rural town in Papua New Guinea known for its highland culture and production of premium coffee.
  • A. Kabuna
    Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
  • B. Kuno
    Kuno is a masculine given name of German origin, historically borne by various nobles and notable figures in German-speaking regions.
  • C. Kuno
    Kuno is the rebellious central character in E.M. Forster’s dystopian science fiction story "The Machine Stops," who challenges the oppressive, technology-dependent society in which he lives.
  • D. Zuchu
    Zuchu is a Tanzanian singer and songwriter known for her Bongo Flava and Afro-pop hits under the WCB Wasafi label.
  • E. Komo
    The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
  • 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_69d806aabde48190899e13e41659cae5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98bcf6d0c819081d078f33e4bdedc completed April 10, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eae675508190991ce5902768c45a completed May 3, 2026, 6:27 a.m.
NEDg Description generation batch_69f6ebb1cd208190970ad8c21e852d93 completed May 3, 2026, 6:31 a.m.
NED2 Entity disambiguation (via description) batch_69f6ec48035881909342b57c061a22a9 completed May 3, 2026, 6:33 a.m.
Created at: April 9, 2026, 9:10 p.m.