Triple

T2094748
Position Surface form Disambiguated ID Type / Status
Subject Fejér County E32753 entity
Predicate containsTown P847 FINISHED
Object Ercsi
Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
 E238551 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: Ercsi | Statement: [Fejér County, containsTown, Ercsi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ercsi
Context triple: [Fejér County, containsTown, Ercsi]
  • A. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • B. Bicske
    Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
  • C. Enying
    Enying is a small town in central Hungary known for its agricultural surroundings and location within Fejér County.
  • D. Sári
    Sári is a Hungarian given name and surname, often used as a diminutive form of names like Sára.
  • E. Kenderes
    Kenderes is a town in Hungary best known as the birthplace and family estate center of Regent Miklós Horthy.
  • 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: Ercsi
Triple: [Fejér County, containsTown, Ercsi]
Generated description
Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ercsi
Target entity description: Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
  • A. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • B. Bicske
    Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
  • C. Enying
    Enying is a small town in central Hungary known for its agricultural surroundings and location within Fejér County.
  • D. Sári
    Sári is a Hungarian given name and surname, often used as a diminutive form of names like Sára.
  • E. Kenderes
    Kenderes is a town in Hungary best known as the birthplace and family estate center of Regent Miklós Horthy.
  • 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_69a885eba0708190999696a45cbec816 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba99ddc48190bb2097b56efb7aca completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58c7cb408190bceee60492292f63 completed March 9, 2026, 5:21 a.m.
NEDg Description generation batch_69ae59892b848190a9cc8b086647ff14 completed March 9, 2026, 5:24 a.m.
NED2 Entity disambiguation (via description) batch_69ae59ff1234819083bbfdce270cb584 completed March 9, 2026, 5:26 a.m.
Created at: March 4, 2026, 7:43 p.m.