Triple

T4002237
Position Surface form Disambiguated ID Type / Status
Subject Zentraler Sanitätsdienst E89439 entity
Predicate hasAbbreviation P43 FINISHED
Object ZSanDstBw E407243 NE FINISHED

How this triple was built (2 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: ZSanDstBw | Statement: [Zentraler Sanitätsdienst, hasAbbreviation, ZSanDstBw]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ZSanDstBw
Context triple: [Zentraler Sanitätsdienst, hasAbbreviation, ZSanDstBw]
  • A. ZSanDstBw chosen
    ZSanDstBw is the official abbreviation for Germany’s Joint Medical Service, the unified medical branch of the Bundeswehr responsible for healthcare and medical support to the armed forces.
  • B. KSAN
    KSAN is the ICAO airport code for San Diego International Airport, a major commercial airport serving the San Diego, California area.
  • C. DAS
    DAS is the acronym for the Defense Attache Service, the U.S. military organization that manages defense attachés and military diplomatic representation at American embassies worldwide.
  • D. BANZSL
    BANZSL is the family of closely related sign languages used in Britain, Australia, and New Zealand, sharing a common historical origin and many linguistic features.
  • E. SAN
    SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69aed9585e788190bec2d39deba3750f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa44125881909f45ecd0a986c581 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5562455a0819097fb6a566f2d387a completed March 14, 2026, 12:35 p.m.
Created at: March 9, 2026, 3:34 p.m.