Triple

T17775328
Position Surface form Disambiguated ID Type / Status
Subject Xiamen University E443752 entity
Predicate abbreviation P43 FINISHED
Object XMU
XMU is a major comprehensive research university located in Xiamen, Fujian, China, known for its strong academic reputation and scenic coastal campus.
 E1286587 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: XMU | Statement: [Xiamen University, abbreviation, XMU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XMU
Context triple: [Xiamen University, abbreviation, XMU]
  • A. XNM
    XNM is the IATA airport-style code assigned to Norwich railway station in Norwich, England, for use in integrated transport and ticketing systems.
  • B. MXM
    MXM is a fashion brand or modeling agency associated with American model and actress Mia Tyler.
  • C. BXM
    BXM is the railway station code for Brussels-South (Bruxelles-Midi / Brussel-Zuid), the main international and domestic rail hub in Brussels, Belgium.
  • D. XU
    XU is the IATA airline designator assigned to African Express Airways, a regional carrier based in Kenya.
  • E. XU
    XU was a clandestine Norwegian intelligence organization that gathered and transmitted vital information to the Allies during the German occupation in World War II.
  • 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: XMU
Triple: [Xiamen University, abbreviation, XMU]
Generated description
XMU is a major comprehensive research university located in Xiamen, Fujian, China, known for its strong academic reputation and scenic coastal campus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: XMU
Target entity description: XMU is a major comprehensive research university located in Xiamen, Fujian, China, known for its strong academic reputation and scenic coastal campus.
  • A. XNM
    XNM is the IATA airport-style code assigned to Norwich railway station in Norwich, England, for use in integrated transport and ticketing systems.
  • B. MXM
    MXM is a fashion brand or modeling agency associated with American model and actress Mia Tyler.
  • C. BXM
    BXM is the railway station code for Brussels-South (Bruxelles-Midi / Brussel-Zuid), the main international and domestic rail hub in Brussels, Belgium.
  • D. XU
    XU is the IATA airline designator assigned to African Express Airways, a regional carrier based in Kenya.
  • E. XU
    XU was a clandestine Norwegian intelligence organization that gathered and transmitted vital information to the Allies during the German occupation in World War II.
  • 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_69d8b9ef17708190bdf7e2adbf14ddc2 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4871c23408190aae7f1a2c77a10cb completed April 19, 2026, 7:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02efc7a3388190bc13861033cb04f3 completed May 12, 2026, 9:15 a.m.
NEDg Description generation batch_6a02f08892e08190a75c4e523366feda completed May 12, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a02f18036788190ad1a2893fd104261 completed May 12, 2026, 9:23 a.m.
Created at: April 10, 2026, 10:12 a.m.