Triple

T37852400
Position Surface form Disambiguated ID Type / Status
Subject Sikorsky S-43 E944089 entity
Predicate civilRegistrationExample P171666 FINISHED
Object NC-16934
NC-16934 was a specific Sikorsky S-43 amphibious flying boat used in civilian aviation during the 1930s.
 E2245395 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: NC-16934 | Statement: [Sikorsky S-43, civilRegistrationExample, NC-16934]
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: NC-16934
Triple: [Sikorsky S-43, civilRegistrationExample, NC-16934]
Generated description
NC-16934 was a specific Sikorsky S-43 amphibious flying boat used in civilian aviation during the 1930s.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: civilRegistrationExample
Context triple: [Sikorsky S-43, civilRegistrationExample, NC-16934]
  • A. civilRegistration
    Indicates the official recording of a person’s vital life event (such as birth, marriage, or death) by a civil authority.
  • B. civilRegistrationCategory
    Indicates the specific type or classification of a civil registration event or record associated with an entity.
  • C. civilRegistrationCodeOf chosen
    Indicates that one entity is the civil registration code assigned to identify another entity in official civil records.
  • D. civilRegistrationPrefixExample
    Indicates that there is an example of a standard prefix used in civil registration identifiers or records.
  • E. religiousRegister
    Indicates that the relationship or action is expressed using language, style, or conventions specific to a religious context or practice.
  • 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_69f76eed4d9c81908b1b71ba9e3b61fe completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbae559a8819086ef839973f8d9b2 completed May 6, 2026, 10:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40fb9448fc8190955703807085f889 completed June 28, 2026, 10:46 a.m.
NEDg Description generation batch_6a40fc690fe08190b4ceaadfe93be2a3 completed June 28, 2026, 10:50 a.m.
NED2 Entity disambiguation (via description) batch_6a40fce18ec08190946c064d27fcf5ad completed June 28, 2026, 10:52 a.m.
PD Predicate disambiguation batch_69fbb1440fa08190abf25ba684f75b6e completed May 6, 2026, 9:23 p.m.
Created at: May 3, 2026, 4:19 p.m.