Triple

T1463084
Position Surface form Disambiguated ID Type / Status
Subject MFF UK E31557 entity
Predicate campus P269 FINISHED
Object Karlov
Karlov is a historic university campus complex in Prague that houses several faculties of Charles University, particularly in the medical and natural sciences.
 E166693 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: Karlov | Statement: [MFF UK, campus, Karlov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Karlov
Context triple: [MFF UK, campus, Karlov]
  • A. Čukarica
    Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
  • B. Lazarevac
    Lazarevac is a suburban municipality of Belgrade in central Serbia, known for its coal mining industry and the Kolubara coal basin.
  • C. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • D. Kuchlak
    Kuchlak is a town in Balochistan, Pakistan, situated near Quetta and known as a local commercial and transit hub in the region.
  • E. Siscia
    Siscia was an important ancient Roman city and military center in the province of Pannonia, located at the site of modern-day Sisak in Croatia.
  • 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: Karlov
Triple: [MFF UK, campus, Karlov]
Generated description
Karlov is a historic university campus complex in Prague that houses several faculties of Charles University, particularly in the medical and natural sciences.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Karlov
Target entity description: Karlov is a historic university campus complex in Prague that houses several faculties of Charles University, particularly in the medical and natural sciences.
  • A. Čukarica
    Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
  • B. Lazarevac
    Lazarevac is a suburban municipality of Belgrade in central Serbia, known for its coal mining industry and the Kolubara coal basin.
  • C. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • D. Kuchlak
    Kuchlak is a town in Balochistan, Pakistan, situated near Quetta and known as a local commercial and transit hub in the region.
  • E. Siscia
    Siscia was an important ancient Roman city and military center in the province of Pannonia, located at the site of modern-day Sisak in Croatia.
  • 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_69a49917dfc081909acdbdf5d684f1ef completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5b6e36c81909c47b2f7e66f17d7 completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e7ab538819090bc3e3ed1bbff64 completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad0f5106fc8190ab03c4e5a0287424 completed March 8, 2026, 5:55 a.m.
NED2 Entity disambiguation (via description) batch_69ad0fa4c7d48190ac84267c16c6eb00 completed March 8, 2026, 5:56 a.m.
Created at: March 1, 2026, 8 p.m.