Triple

T7985579
Position Surface form Disambiguated ID Type / Status
Subject Apache Storm E185674 entity
Predicate supportsIntegrationWith P5090 FINISHED
Object MongoDB E360848 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: MongoDB | Statement: [Apache Storm, supportsIntegrationWith, MongoDB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MongoDB
Context triple: [Apache Storm, supportsIntegrationWith, MongoDB]
  • A. Mongo
    Mongo is a major Bantu language spoken primarily in the Democratic Republic of the Congo by the Mongo people.
  • B. Mongo
    Mongo is the first child of Claireece "Precious" Jones in the novel and film "Precious," born with severe disabilities as a result of incestuous abuse.
  • C. Mongo
    Mongo is the nickname of Steve "Mongo" McMichael, a former NFL defensive tackle and professional wrestler best known for his time with the Chicago Bears and WCW.
  • D. MongoDB database chosen
    MongoDB database is a popular open-source NoSQL document-oriented database designed for scalability, flexibility, and high performance in modern applications.
  • E. MongoDB Cloud
    MongoDB Cloud is a fully managed cloud database platform that provides scalable, secure, and globally distributed MongoDB database services along with tools for data management, monitoring, and analytics.
  • 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_69ca829a2cfc819083d591d58ec04075 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c4a55b881909a96133e56c0dffa completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbe17811081909c19f18c853617af completed April 1, 2026, 6:41 a.m.
Created at: March 30, 2026, 5:15 p.m.