Triple

T6860750
Position Surface form Disambiguated ID Type / Status
Subject Poltava E158268 entity
Predicate hasLandmark P105 FINISHED
Object White Arbor
White Arbor is a notable architectural landmark and popular viewpoint in the Ukrainian city of Poltava.
 E624332 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: White Arbor | Statement: [Poltava, hasLandmark, White Arbor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: White Arbor
Context triple: [Poltava, hasLandmark, White Arbor]
  • A. Gardiner
    Gardiner is a commonly used short name for the Gardiner Expressway, a major elevated highway running along Toronto’s waterfront.
  • B. Gardiner
    Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Orono
    Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
  • D. Orono
    Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
  • E. Southwood
    Southwood is a suburban residential area within the Borough of Rushmoor in Hampshire, England.
  • 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: White Arbor
Triple: [Poltava, hasLandmark, White Arbor]
Generated description
White Arbor is a notable architectural landmark and popular viewpoint in the Ukrainian city of Poltava.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: White Arbor
Target entity description: White Arbor is a notable architectural landmark and popular viewpoint in the Ukrainian city of Poltava.
  • A. Gardiner
    Gardiner is a commonly used short name for the Gardiner Expressway, a major elevated highway running along Toronto’s waterfront.
  • B. Gardiner
    Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Orono
    Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
  • D. Orono
    Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
  • E. Southwood
    Southwood is a suburban residential area within the Borough of Rushmoor in Hampshire, England.
  • 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_69c68830cdbc8190a8301c7a9d9f651a completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d88659c8819084916663219a8198 completed March 27, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c72fed3e788190b2b68fc93173f73e completed March 28, 2026, 1:33 a.m.
NEDg Description generation batch_69c7361177ac8190b1e06cb15d258d0f completed March 28, 2026, 1:59 a.m.
NED2 Entity disambiguation (via description) batch_69c7381015a081909f9b32732f826d2c completed March 28, 2026, 2:08 a.m.
Created at: March 27, 2026, 2:21 p.m.