Triple

T1539816
Position Surface form Disambiguated ID Type / Status
Subject Louisiana E32838 entity
Predicate hasMajorCity P316 FINISHED
Object Monroe
Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
 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: [Louisiana, hasMajorCity, Monroe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Monroe
Context triple: [Louisiana, hasMajorCity, Monroe]
  • A. Monroe
    Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
  • B. Monroe
    Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
  • C. Monroe
    Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
  • D. Gardiner
    Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • E. Madison
    Madison is a common English surname and given name, historically associated with U.S. President James Madison and now widely used as a first name, especially for girls.
  • 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: [Louisiana, hasMajorCity, Monroe]
Generated description
Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Monroe
Target entity description: Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
  • A. Monroe
    Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
  • B. Monroe
    Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
  • C. Monroe
    Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
  • D. Gardiner
    Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • E. Madison
    Madison is a suburban city in northern Alabama known for its proximity to Huntsville and its strong schools and residential communities.
  • 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_69a885ed29088190a3c2d5a3d100c16e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9083c942481909168394b6674d82b completed March 5, 2026, 4:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad468f9a8c8190817910c2955b4338 completed March 8, 2026, 9:51 a.m.
NEDg Description generation batch_69ad48c2ebc0819095d4a4d68d221558 completed March 8, 2026, 10 a.m.
NED2 Entity disambiguation (via description) batch_69ad4915911081908b11ae52783d3111 completed March 8, 2026, 10:01 a.m.
Created at: March 4, 2026, 7:26 p.m.