Triple

T4253300
Position Surface form Disambiguated ID Type / Status
Subject Luxor Governorate E95909 entity
Predicate hasCity P316 FINISHED
Object Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
 E423399 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: Tiba | Statement: [Luxor Governorate, hasCity, Tiba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tiba
Context triple: [Luxor Governorate, hasCity, Tiba]
  • A. Tantamani
    Tantamani was a Kushite king of the 25th Dynasty of Egypt, known for his brief attempt to restore Nubian control over Egypt before being driven back by the Assyrians.
  • B. 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.
  • C. Tura
    Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
  • D. Tivissa
    Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • E. Tianeti
    Tianeti is a small town and administrative center in eastern Georgia, situated in the mountainous Mtskheta-Mtianeti region.
  • 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: Tiba
Triple: [Luxor Governorate, hasCity, Tiba]
Generated description
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tiba
Target entity description: Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
  • A. Tantamani
    Tantamani was a Kushite king of the 25th Dynasty of Egypt, known for his brief attempt to restore Nubian control over Egypt before being driven back by the Assyrians.
  • B. 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.
  • C. Tura
    Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
  • D. Tivissa
    Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • E. Tianeti
    Tianeti is a small town and administrative center in eastern Georgia, situated in the mountainous Mtskheta-Mtianeti region.
  • 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_69b3453f759881909b91f01a1e82c036 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34ebe6fbc8190a89269b478b3f435 completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a8845e6081908bbf1aef2a2da754 completed March 14, 2026, 6:27 p.m.
NEDg Description generation batch_69b5a8f374fc8190830286dfadc9bdbb completed March 14, 2026, 6:29 p.m.
NED2 Entity disambiguation (via description) batch_69b5a99c9310819084b973aecb1886fe completed March 14, 2026, 6:31 p.m.
Created at: March 12, 2026, 11:06 p.m.