Triple

T11679874
Position Surface form Disambiguated ID Type / Status
Subject V. Mapa station E277586 entity
Predicate hasStationCode P1289 FINISHED
Object VM
VM is the station code assigned to V. Mapa station in the Manila Light Rail Transit system.
 E940550 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: VM | Statement: [V. Mapa station, hasStationCode, VM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VM
Context triple: [V. Mapa station, hasStationCode, VM]
  • A. VM
    VM is IBM's family of mainframe virtual machine operating systems designed to run multiple operating environments concurrently on System/370 and successor hardware.
  • B. VM
    VM is the abbreviation for the Volksmarine, the navy of the former German Democratic Republic (East Germany).
  • C. VMX
    VMX is a vector processing extension to the PowerPC architecture designed to accelerate multimedia, signal processing, and other parallelizable computations.
  • D. VMC
    VMC is the Venus Monitoring Camera, a wide-angle imaging instrument on the European Space Agency’s Venus Express spacecraft used to study Venus’s atmosphere and cloud dynamics.
  • E. Virtual Machine Manager
    Virtual Machine Manager is a Microsoft System Center component used to centrally manage, configure, and deploy virtualized datacenter resources across Hyper-V and other virtualization platforms.
  • 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: VM
Triple: [V. Mapa station, hasStationCode, VM]
Generated description
VM is the station code assigned to V. Mapa station in the Manila Light Rail Transit system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VM
Target entity description: VM is the station code assigned to V. Mapa station in the Manila Light Rail Transit system.
  • A. VM
    VM is the abbreviation for the Volksmarine, the navy of the former German Democratic Republic (East Germany).
  • B. VM
    VM is IBM's family of mainframe virtual machine operating systems designed to run multiple operating environments concurrently on System/370 and successor hardware.
  • C. VMX
    VMX is a vector processing extension to the PowerPC architecture designed to accelerate multimedia, signal processing, and other parallelizable computations.
  • D. VMC
    VMC is the Venus Monitoring Camera, a wide-angle imaging instrument on the European Space Agency’s Venus Express spacecraft used to study Venus’s atmosphere and cloud dynamics.
  • E. Virtual Machine Manager
    Virtual Machine Manager is a Microsoft System Center component used to centrally manage, configure, and deploy virtualized datacenter resources across Hyper-V and other virtualization platforms.
  • 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_69d6aafd0a448190b44da30af8c6c519 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a461b0908190bef4e1c6777affcf completed April 10, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef14007dd08190b60640be9949ca26 completed April 27, 2026, 7:45 a.m.
NEDg Description generation batch_69ef35527f908190b681afdae3aec319 completed April 27, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69ef51ec07ec8190b5cd97cf909388f0 completed April 27, 2026, 12:09 p.m.
Created at: April 8, 2026, 9:40 p.m.