Triple

T3512723
Position Surface form Disambiguated ID Type / Status
Subject Recruit Training Regiment E74232 entity
Predicate nickname P55 FINISHED
Object RTR
RTR is the commonly used nickname for the Recruit Training Regiment, a military unit responsible for initial training of new recruits.
E366335 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: RTR | Statement: [Recruit Training Regiment, nickname, RTR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RTR
Context triple: [Recruit Training Regiment, nickname, RTR]
  • A. TRR
    TRR is the IATA airport code for Trincomalee Airport in Sri Lanka.
  • B. RTA
    RTA is the common abbreviation for the Regional Transportation Authority of Northeastern Illinois, the agency that oversees public transit services in the Chicago metropolitan area.
  • C. TRV
    TRV is the stock ticker symbol for The Travelers Companies, a major U.S.-based insurance provider known for its property and casualty insurance products.
  • D. RTG
    RTG (Radioisotope Thermoelectric Generator) is a long-lived power system that converts heat from the natural decay of radioactive material into electricity, commonly used on deep-space missions like Voyager 2.
  • E. RTM
    RTM is the commonly used abbreviation for Rosetta Terminology Mapping, a system for standardizing and aligning terminology across different datasets or domains.
  • 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: RTR
Triple: [Recruit Training Regiment, nickname, RTR]
Generated description
RTR is the commonly used nickname for the Recruit Training Regiment, a military unit responsible for initial training of new recruits.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: RTR
Target entity description: RTR is the commonly used nickname for the Recruit Training Regiment, a military unit responsible for initial training of new recruits.
  • A. TRR
    TRR is the IATA airport code for Trincomalee Airport in Sri Lanka.
  • B. RTA
    RTA is the common abbreviation for the Regional Transportation Authority of Northeastern Illinois, the agency that oversees public transit services in the Chicago metropolitan area.
  • C. TRV
    TRV is the stock ticker symbol for The Travelers Companies, a major U.S.-based insurance provider known for its property and casualty insurance products.
  • D. RTG
    RTG (Radioisotope Thermoelectric Generator) is a long-lived power system that converts heat from the natural decay of radioactive material into electricity, commonly used on deep-space missions like Voyager 2.
  • E. TRB
    TRB was the former stock ticker symbol for Tribune Company, a major American media conglomerate known for owning newspapers and television stations.
  • 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_69ad85cfb5c881909c9a2edd9d6043cc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc10b6b48190bedfed6d34afc425 completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e76c3a08190831402ff0c680196 completed March 13, 2026, 3:03 a.m.
NEDg Description generation batch_69b38232467c81909eb831bb0747cb77 completed March 13, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_69b385f078d48190a72e61c59aa27e43 completed March 13, 2026, 3:35 a.m.
Created at: March 8, 2026, 3:19 p.m.