Triple

T8792394
Position Surface form Disambiguated ID Type / Status
Subject ULM School of Aviation E209199 entity
Predicate city P40 FINISHED
Object Monroe
Monroe is a city in northeastern Louisiana that serves as a regional hub for education, healthcare, and transportation.
 E180923 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: Monroe | Statement: [ULM School of Aviation, city, Monroe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Monroe
Context triple: [ULM School of Aviation, city, Monroe]
  • A. Monroe
    Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
  • B. Monroe
    Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
  • C. Monroe
    Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
  • D. Monroe
    Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
  • E. Monroe
    Monroe is a given name used as a first name, notably borne by actor Jackson Rathbone.
  • 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: Monroe
Triple: [ULM School of Aviation, city, Monroe]
Generated description
Monroe is a city in northeastern Louisiana that serves as a regional hub for education, healthcare, and transportation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Monroe
Target entity description: Monroe is a city in northeastern Louisiana that serves as a regional hub for education, healthcare, and transportation.
  • A. Monroe chosen
    Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
  • B. Monroe
    Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
  • C. Monroe
    Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
  • D. Monroe
    Monroe is a small city in Washington State known for its location in the Skykomish River Valley and its role as a regional hub for outdoor recreation and community events.
  • E. Monroe
    Monroe is a Chicago 'L' rapid transit station located in the Loop, serving the CTA Blue Line.
  • F. None of above.

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_69ca836240888190a62b262e56a69d2f completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f8f9eb08190bd709f3c8e09412f completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6f493f648190b0e9392f1abb44a1 completed April 3, 2026, 7:42 a.m.
NEDg Description generation batch_69cf708dbd54819099efa4b5729d6298 completed April 3, 2026, 7:47 a.m.
NED2 Entity disambiguation (via description) batch_69cf7163e2088190bf252896cc4036b2 completed April 3, 2026, 7:51 a.m.
Created at: March 30, 2026, 6:43 p.m.