Triple

T22162042
Position Surface form Disambiguated ID Type / Status
Subject Mymensingh Railway Station E547693 entity
Predicate connectsTo P845 FINISHED
Object Netrokona
Netrokona is a district in north-central Bangladesh known for its rural landscapes, rivers, and agricultural communities.
 E1523559 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: Netrokona | Statement: [Mymensingh Railway Station, connectsTo, Netrokona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Netrokona
Context triple: [Mymensingh Railway Station, connectsTo, Netrokona]
  • A. Nitelink
    Nitelink is Dublin’s late-night bus service network, providing after-hours public transport on key routes across the city and suburbs.
  • B. Netze
    Netze is the German name for the Noteć, a river in north-central Poland that is a tributary of the Warta.
  • C. Netphen
    Netphen is a small town in the Siegerland region of North Rhine-Westphalia, Germany, known for its surrounding forests and role as a local administrative and economic center.
  • D. NovaLink
    NovaLink is an IBM Power Systems virtualization management interface that streamlines the deployment and control of virtual machines and resources on Power hardware.
  • E. TNET
    TNET is the stock ticker symbol for Telenet Group, a Belgian telecommunications and entertainment services provider.
  • 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: Netrokona
Triple: [Mymensingh Railway Station, connectsTo, Netrokona]
Generated description
Netrokona is a district in north-central Bangladesh known for its rural landscapes, rivers, and agricultural communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Netrokona
Target entity description: Netrokona is a district in north-central Bangladesh known for its rural landscapes, rivers, and agricultural communities.
  • A. Nitelink
    Nitelink is Dublin’s late-night bus service network, providing after-hours public transport on key routes across the city and suburbs.
  • B. Netze
    Netze is the German name for the Noteć, a river in north-central Poland that is a tributary of the Warta.
  • C. Netphen
    Netphen is a small town in the Siegerland region of North Rhine-Westphalia, Germany, known for its surrounding forests and role as a local administrative and economic center.
  • D. NovaLink
    NovaLink is an IBM Power Systems virtualization management interface that streamlines the deployment and control of virtual machines and resources on Power hardware.
  • E. TNET
    TNET is the stock ticker symbol for Telenet Group, a Belgian telecommunications and entertainment services provider.
  • 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_69e11e3c4c5c81908d336165816b12e0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12a2e47a88190a3b5c05398605f68 completed April 28, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a9eba95d48190bc19f69531dbb752 completed May 18, 2026, 5:08 a.m.
NEDg Description generation batch_6a0aa06763b48190ab0ecdf90de4f463 completed May 18, 2026, 5:15 a.m.
NED2 Entity disambiguation (via description) batch_6a0aa0c4d1648190b04e6a6cc7afd756 completed May 18, 2026, 5:16 a.m.
Created at: April 16, 2026, 8:34 p.m.