Triple

T3335890
Position Surface form Disambiguated ID Type / Status
Subject Buda E70138 entity
Predicate hasPart P35 FINISHED
Object Tabán
Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
 E349575 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: Tabán | Statement: [Buda, hasPart, Tabán]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tabán
Context triple: [Buda, hasPart, Tabán]
  • A. Mutasa
    Mutasa is a town located in Zimbabwe’s eastern Manicaland Province, known for its rural communities and proximity to the Eastern Highlands.
  • B. Tajewala
    Tajewala is a village in the Yamunanagar district of Haryana, India, historically known for the Tajewala Barrage on the Yamuna River, which was later replaced by the nearby Hathni Kund Barrage.
  • C. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • D. Tura
    Tura is a prominent town in the Indian state of Meghalaya, serving as a major administrative, cultural, and economic center in the Garo Hills region.
  • E. Tura
    Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
  • 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: Tabán
Triple: [Buda, hasPart, Tabán]
Generated description
Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tabán
Target entity description: Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
  • A. Mutasa
    Mutasa is a town located in Zimbabwe’s eastern Manicaland Province, known for its rural communities and proximity to the Eastern Highlands.
  • B. Tajewala
    Tajewala is a village in the Yamunanagar district of Haryana, India, historically known for the Tajewala Barrage on the Yamuna River, which was later replaced by the nearby Hathni Kund Barrage.
  • C. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • D. Tura
    Tura is a prominent town in the Indian state of Meghalaya, serving as a major administrative, cultural, and economic center in the Garo Hills region.
  • E. Tura
    Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb197bd0481909a5cf7eab386e176 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a885d148190a101ce50d5d87f53 completed March 12, 2026, 7:56 p.m.
NEDg Description generation batch_69b31c393f20819098d5761372d6a980 completed March 12, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69b3206be2748190874560701dc1ed18 completed March 12, 2026, 8:22 p.m.
Created at: March 8, 2026, 3:12 p.m.