Triple

T766824
Position Surface form Disambiguated ID Type / Status
Subject Spanish Air Force E16192 entity
Predicate hasUnit P35 FINISHED
Object Ala 49
Ala 49 is a unit of the Spanish Air Force, likely an air wing responsible for operating and supporting specific aircraft and missions within Spain’s military aviation structure.
 E91657 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: Ala 49 | Statement: [Spanish Air Force, hasUnit, Ala 49]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ala 49
Context triple: [Spanish Air Force, hasUnit, Ala 49]
  • A. Ala 48
    Ala 48 is a wing of the Spanish Air Force responsible for operating transport and support aircraft in national and international missions.
  • B. Alta
    Alta is a town in northern Norway known for its Arctic location, winter sports, and proximity to the Northern Lights.
  • C. Ala 11
    Ala 11 is a fighter wing of the Spanish Air Force known for operating modern combat aircraft and contributing to Spain’s air defense and tactical operations.
  • D. Arrah
    Arrah is a historic town in the Indian state of Bihar, known for its role as a key site of conflict during the Indian Rebellion of 1857.
  • E. Pinales
    Pinales is the botanical order of coniferous trees and shrubs that includes pines, firs, spruces, and related needle-leaved, cone-bearing plants.
  • 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: Ala 49
Triple: [Spanish Air Force, hasUnit, Ala 49]
Generated description
Ala 49 is a unit of the Spanish Air Force, likely an air wing responsible for operating and supporting specific aircraft and missions within Spain’s military aviation structure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ala 49
Target entity description: Ala 49 is a unit of the Spanish Air Force, likely an air wing responsible for operating and supporting specific aircraft and missions within Spain’s military aviation structure.
  • A. Ala 48
    Ala 48 is a wing of the Spanish Air Force responsible for operating transport and support aircraft in national and international missions.
  • B. Alta
    Alta is a town in northern Norway known for its Arctic location, winter sports, and proximity to the Northern Lights.
  • C. Ala 11
    Ala 11 is a fighter wing of the Spanish Air Force known for operating modern combat aircraft and contributing to Spain’s air defense and tactical operations.
  • D. Arrah
    Arrah is a historic town in the Indian state of Bihar, known for its role as a key site of conflict during the Indian Rebellion of 1857.
  • E. Pinales
    Pinales is the botanical order of coniferous trees and shrubs that includes pines, firs, spruces, and related needle-leaved, cone-bearing plants.
  • 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_69a493684ee48190bd43b7c78da4aec8 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a6a0fee08190bf365d14c007e008 completed March 1, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69a66d994aa081908b748544f5d7f6ed completed March 3, 2026, 5:11 a.m.
NEDg Description generation batch_69a66defe41881909cdb3fe3768052ba completed March 3, 2026, 5:13 a.m.
NED2 Entity disambiguation (via description) batch_69a66e61b1348190be290f04b18e67cb completed March 3, 2026, 5:15 a.m.
Created at: March 1, 2026, 7:37 p.m.