Triple

T12128797
Position Surface form Disambiguated ID Type / Status
Subject Mersin Province E288878 entity
Predicate hasDistrict P459 FINISHED
Object Mut
Mut is a district and town in Turkey’s Mersin Province, known for its agricultural production—especially apricots—and its historical sites dating back to ancient times.
 E967919 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: Mut | Statement: [Mersin Province, hasDistrict, Mut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mut
Context triple: [Mersin Province, hasDistrict, Mut]
  • A. Mut
    Mut is an ancient Egyptian mother goddess associated with kingship and protection, prominently worshipped at Thebes as a principal consort of Amun.
  • B. Mut
    Mut is a town in Egypt’s Western Desert that serves as the main administrative and population center of the Dakhla Oasis.
  • C. Mir
    Mir was a Soviet and later Russian modular space station that served as a long-term research outpost in low Earth orbit from 1986 to 2001.
  • D. Mir
    Mir is a historic town in present-day Belarus, known for its multicultural heritage and the UNESCO-listed Mir Castle Complex.
  • E. Mir
    Mir is a traditional South Asian noble title historically used by rulers and aristocrats, particularly in regions such as Sindh under dynasties like the Talpurs.
  • 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: Mut
Triple: [Mersin Province, hasDistrict, Mut]
Generated description
Mut is a district and town in Turkey’s Mersin Province, known for its agricultural production—especially apricots—and its historical sites dating back to ancient times.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mut
Target entity description: Mut is a district and town in Turkey’s Mersin Province, known for its agricultural production—especially apricots—and its historical sites dating back to ancient times.
  • A. Mut
    Mut is an ancient Egyptian mother goddess associated with kingship and protection, prominently worshipped at Thebes as a principal consort of Amun.
  • B. Mut
    Mut is a town in Egypt’s Western Desert that serves as the main administrative and population center of the Dakhla Oasis.
  • C. Mir
    Mir was a Soviet and later Russian modular space station that served as a long-term research outpost in low Earth orbit from 1986 to 2001.
  • D. Mir
    Mir is a historic town in present-day Belarus, known for its multicultural heritage and the UNESCO-listed Mir Castle Complex.
  • E. Mir
    Mir is a traditional South Asian noble title historically used by rulers and aristocrats, particularly in regions such as Sindh under dynasties like the Talpurs.
  • 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_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9158a2c2c8190aaff9d0cce177565 completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f68ac15c81908388dd3194e9dfc4 completed May 2, 2026, 1:05 p.m.
NEDg Description generation batch_69f6048d6f24819093862fb46f9938f1 completed May 2, 2026, 2:05 p.m.
NED2 Entity disambiguation (via description) batch_69f6056e3fdc81908e6e97c4c37a18bb completed May 2, 2026, 2:08 p.m.
Created at: April 8, 2026, 9:49 p.m.