Triple

T19394018
Position Surface form Disambiguated ID Type / Status
Subject Permanent Multipurpose Module E485140 entity
Predicate alsoKnownAs P39 FINISHED
Object PMM
PMM is a repurposed cargo module attached to the International Space Station that provides additional storage and workspace for the crew.
 E1373958 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: PMM | Statement: [Permanent Multipurpose Module, alsoKnownAs, PMM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PMM
Context triple: [Permanent Multipurpose Module, alsoKnownAs, PMM]
  • A. PMM
    PMM is the commonly used acronym for Perpignan Méditerranée Métropole, an intercommunal urban community centered on the city of Perpignan in southern France.
  • B. PMB
    PMB is the abbreviation for Pure Michigan Byway, a designation for particularly scenic or culturally significant roads in the state of Michigan.
  • C. PMO
    PMO is the IATA airport code for Falcone–Borsellino Airport serving Palermo, Sicily, Italy.
  • D. PMO
    PMO is the central executive body that supports and coordinates the work of Singapore’s Prime Minister and the Cabinet.
  • E. PDM
    PDM is a modern Python package and dependency manager that emphasizes PEP 582 support and a streamlined, pyproject.toml-based workflow.
  • 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: PMM
Triple: [Permanent Multipurpose Module, alsoKnownAs, PMM]
Generated description
PMM is a repurposed cargo module attached to the International Space Station that provides additional storage and workspace for the crew.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PMM
Target entity description: PMM is a repurposed cargo module attached to the International Space Station that provides additional storage and workspace for the crew.
  • A. PMM
    PMM is the commonly used acronym for Perpignan Méditerranée Métropole, an intercommunal urban community centered on the city of Perpignan in southern France.
  • B. PMB
    PMB is the abbreviation for Pure Michigan Byway, a designation for particularly scenic or culturally significant roads in the state of Michigan.
  • C. PMO
    PMO is the IATA airport code for Falcone–Borsellino Airport serving Palermo, Sicily, Italy.
  • D. PMO
    PMO is the central executive body that supports and coordinates the work of Singapore’s Prime Minister and the Cabinet.
  • E. PDM
    PDM is a modern Python package and dependency manager that emphasizes PEP 582 support and a streamlined, pyproject.toml-based workflow.
  • 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_69d8e8d5162481909db12435d9535c1a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e61b47630881909ba390888b8779f6 completed April 20, 2026, 12:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a072b8b87a881909b3651f98ab49cdb completed May 15, 2026, 2:19 p.m.
NEDg Description generation batch_6a072eb2d5b08190a0cafb1427cfd16d completed May 15, 2026, 2:33 p.m.
NED2 Entity disambiguation (via description) batch_6a072f6caef48190acb52960ebde9ace completed May 15, 2026, 2:36 p.m.
Created at: April 10, 2026, 1:36 p.m.