Triple

T8718031
Position Surface form Disambiguated ID Type / Status
Subject Warning Decision Training Division E206942 entity
Predicate hasAbbreviation P43 FINISHED
Object WDTD
WDTD is the Warning Decision Training Division, a U.S. National Weather Service unit that develops and delivers training to improve severe weather forecasting and warning operations.
 E754007 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: WDTD | Statement: [Warning Decision Training Division, hasAbbreviation, WDTD]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WDTD
Context triple: [Warning Decision Training Division, hasAbbreviation, WDTD]
  • A. WADT
    WADT is the ICAO airport code for Tambolaka Airport, a regional airport serving the island of Sumba in Indonesia.
  • B. WTW
    WTW is the abbreviation for "Walking Together on the Way," an ecumenical document focused on fostering unity and dialogue among Christian traditions.
  • C. WTDL
    WTDL is the radio callsign assigned to the NOAA research vessel Pisces, used for its identification in maritime communications.
  • D. WATH
    WATH is a local radio station serving the Athens, Ohio area with news, talk, and music programming.
  • E. WTM
    WTM is the vehicle registration code used on license plates for vehicles registered in the Wittmund district of Lower Saxony, Germany.
  • 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: WDTD
Triple: [Warning Decision Training Division, hasAbbreviation, WDTD]
Generated description
WDTD is the Warning Decision Training Division, a U.S. National Weather Service unit that develops and delivers training to improve severe weather forecasting and warning operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WDTD
Target entity description: WDTD is the Warning Decision Training Division, a U.S. National Weather Service unit that develops and delivers training to improve severe weather forecasting and warning operations.
  • A. WADT
    WADT is the ICAO airport code for Tambolaka Airport, a regional airport serving the island of Sumba in Indonesia.
  • B. WTW
    WTW is the abbreviation for "Walking Together on the Way," an ecumenical document focused on fostering unity and dialogue among Christian traditions.
  • C. WTDL
    WTDL is the radio callsign assigned to the NOAA research vessel Pisces, used for its identification in maritime communications.
  • D. WATH
    WATH is a local radio station serving the Athens, Ohio area with news, talk, and music programming.
  • E. WTM
    WTM is the vehicle registration code used on license plates for vehicles registered in the Wittmund district of Lower Saxony, Germany.
  • 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_69ca83572d4881909bef3be2b578d539 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5cdac6988190b9f9cc1f350aae53 completed March 31, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28ec7148819096f7fa33e4588b62 completed April 3, 2026, 2:41 a.m.
NEDg Description generation batch_69cf2bd222b08190907ba7e98991996e completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2fcb5e7c819086b441d1ef4fc368 completed April 3, 2026, 3:11 a.m.
Created at: March 30, 2026, 6:36 p.m.