Triple

T29340571
Position Surface form Disambiguated ID Type / Status
Subject Land of the Dead (Grim Fandango) E744024 entity
Predicate containsTransportSystem P941 FINISHED
Object Number Nine train
The Number Nine train is a luxurious express train in the video game Grim Fandango that swiftly carries virtuous souls through the Land of the Dead to their final resting place.
E1861602 NE FINISHED

How this triple was built (3 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: Number Nine train | Statement: [Land of the Dead (Grim Fandango), containsTransportSystem, Number Nine train]
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: Number Nine train
Triple: [Land of the Dead (Grim Fandango), containsTransportSystem, Number Nine train]
Generated description
The Number Nine train is a luxurious express train in the video game Grim Fandango that swiftly carries virtuous souls through the Land of the Dead to their final resting place.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: containsTransportSystem
Context triple: [Land of the Dead (Grim Fandango), containsTransportSystem, Number Nine train]
  • A. hasTransportationSystem chosen
    Indicates that an entity possesses, operates, or is served by an organized system for transporting people or goods.
  • B. hasTransportationElement
    Indicates that one entity includes, involves, or is associated with a specific transportation-related component or feature.
  • C. hasTransportRoute
    Indicates that there exists a designated transportation connection or route linking one entity to another.
  • D. hasTransporters
    Indicates that one entity possesses, provides, or is equipped with one or more means of transportation for another entity or purpose.
  • E. isTransport
    Indicates that one entity serves as a means or method for conveying or carrying another entity from one place to another.
  • F. None of above.

Provenance (6 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_69f09126cfcc8190899b16fbf3c2bf7b completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69fe78e545888190a239af1a84280fa0 completed May 8, 2026, 11:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25a8811d648190bb0ad8f068cd5085 completed June 7, 2026, 5:21 p.m.
NEDg Description generation batch_6a25ac8968648190b075ba14bd35f06e completed June 7, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_6a25b11292e48190823e673d9d093664 completed June 7, 2026, 5:57 p.m.
PD Predicate disambiguation batch_69fe7842742081908043eb950ed69f92 completed May 8, 2026, 11:56 p.m.
Created at: April 28, 2026, 1:33 p.m.