Triple

T12345622
Position Surface form Disambiguated ID Type / Status
Subject H Line (Denver light rail) E294345 entity
Predicate hasRouteDesignation P5539 FINISHED
Object H
H is a light rail service designation used for one of the lines in Denver’s Regional Transportation District (RTD) rail network.
 E977999 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: H | Statement: [H Line (Denver light rail), hasRouteDesignation, H]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: H
Context triple: [H Line (Denver light rail), hasRouteDesignation, H]
  • A. H
    H is the commonly used abbreviation for the Conservative Party of Norway, a major center-right political party in the country.
  • B. H.
    H. is an individual whose given name is represented by the initial "H."
  • C. HY
    HY is the commonly used abbreviation for the University of Helsinki, a major research university in Finland.
  • D. HL
    HL is the vehicle registration code used on license plates for the German city of Lübeck.
  • E. HB
    HB is the second-generation Holden Torana small family car series produced in the late 1960s, known for introducing more modern styling and engineering updates over its predecessor.
  • 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: H
Triple: [H Line (Denver light rail), hasRouteDesignation, H]
Generated description
H is a light rail service designation used for one of the lines in Denver’s Regional Transportation District (RTD) rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: H
Target entity description: H is a light rail service designation used for one of the lines in Denver’s Regional Transportation District (RTD) rail network.
  • A. H
    H is the commonly used abbreviation for the Conservative Party of Norway, a major center-right political party in the country.
  • B. H.
    H. is an individual whose given name is represented by the initial "H."
  • C. HY
    HY is the commonly used abbreviation for the University of Helsinki, a major research university in Finland.
  • D. HL
    HL is the vehicle registration code used on license plates for the German city of Lübeck.
  • E. HB
    HB is the official vehicle registration code used on license plates for the German city-state of Bremen.
  • 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_69d6ab6ccbec8190b09e2d357aa80064 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f7a4a448190aa70d66dc2f406c1 completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f62aac9edc8190ab1740d23af1b250 completed May 2, 2026, 4:47 p.m.
NEDg Description generation batch_69f62c55aacc8190a0544306825bdfab completed May 2, 2026, 4:54 p.m.
NED2 Entity disambiguation (via description) batch_69f62d4d0b8881908aa6b67db7d14609 completed May 2, 2026, 4:58 p.m.
Created at: April 8, 2026, 9:53 p.m.