Triple

T5336141
Position Surface form Disambiguated ID Type / Status
Subject Helmand Province E123830 entity
Predicate containsCity P294 FINISHED
Object Sangin
Sangin is a town in southern Afghanistan that gained notoriety as a major battleground during the Afghan conflict, particularly involving British and U.S. forces.
 E512987 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: Sangin | Statement: [Helmand Province, containsCity, Sangin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sangin
Context triple: [Helmand Province, containsCity, Sangin]
  • A. Rano
    Rano is a historic town and traditional emirate in northern Nigeria, located within Kano State.
  • B. Ryti
    Ryti is the surname of Risto Ryti, who served as President of Finland during World War II.
  • C. Madruga
    Madruga is a municipality in western Cuba known for its rural character and location within the historical region surrounding Havana.
  • D. Narantaka
    Narantaka is a lesser-known demon warrior from the Hindu epic Ramayana, depicted as one of Ravana’s sons who fights in the great battle of Lanka.
  • E. Mornant
    Mornant is a commune in eastern France located in the Rhône department, known for its rural character and proximity to the Lyon metropolitan area.
  • 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: Sangin
Triple: [Helmand Province, containsCity, Sangin]
Generated description
Sangin is a town in southern Afghanistan that gained notoriety as a major battleground during the Afghan conflict, particularly involving British and U.S. forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sangin
Target entity description: Sangin is a town in southern Afghanistan that gained notoriety as a major battleground during the Afghan conflict, particularly involving British and U.S. forces.
  • A. Rano
    Rano is a historic town and traditional emirate in northern Nigeria, located within Kano State.
  • B. Ryti
    Ryti is the surname of Risto Ryti, who served as President of Finland during World War II.
  • C. Madruga
    Madruga is a municipality in western Cuba known for its rural character and location within the historical region surrounding Havana.
  • D. Narantaka
    Narantaka is a lesser-known demon warrior from the Hindu epic Ramayana, depicted as one of Ravana’s sons who fights in the great battle of Lanka.
  • E. Mornant
    Mornant is a commune in eastern France located in the Rhône department, known for its rural character and proximity to the Lyon metropolitan area.
  • 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_69bd464b07f8819095aa76577c9829e4 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd85b104c081908b81236a0142e1c8 completed March 20, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18be4bb88190a2b83e51716e677e completed March 21, 2026, 10:16 p.m.
NEDg Description generation batch_69bf194c53a48190b0895bbe9aa2f6f1 completed March 21, 2026, 10:18 p.m.
NED2 Entity disambiguation (via description) batch_69bf1a198418819089b25102733f9191 completed March 21, 2026, 10:22 p.m.
Created at: March 20, 2026, 2 p.m.