Triple

T1613141
Position Surface form Disambiguated ID Type / Status
Subject NestJS E34654 entity
Predicate usesTool P98 FINISHED
Object Mongoose
Mongoose is a popular Node.js object data modeling (ODM) library that provides a schema-based solution for modeling and interacting with MongoDB databases.
 E183384 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: Mongoose | Statement: [NestJS, usesTool, Mongoose]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mongoose
Context triple: [NestJS, usesTool, Mongoose]
  • A. Boas
    Boas is a surname most prominently associated with Franz Boas, the pioneering anthropologist often regarded as the father of American anthropology.
  • B. Gecko
    Gecko is Mozilla’s open-source web browser engine that powers the rendering and functionality of Firefox and several other applications.
  • C. Colubrina
    Colubrina is a genus of flowering shrubs and small trees known for their hard wood and occurrence in tropical and subtropical regions.
  • D. Pongo
    Pongo is the genus of great apes commonly known as orangutans, native to the rainforests of Borneo and Sumatra.
  • E. Miga
    Miga is one of the official mascots of the 2010 Winter Olympics in Vancouver, depicted as a playful sea bear inspired by orcas and First Nations legends.
  • 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: Mongoose
Triple: [NestJS, usesTool, Mongoose]
Generated description
Mongoose is a popular Node.js object data modeling (ODM) library that provides a schema-based solution for modeling and interacting with MongoDB databases.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mongoose
Target entity description: Mongoose is a popular Node.js object data modeling (ODM) library that provides a schema-based solution for modeling and interacting with MongoDB databases.
  • A. Boas
    Boas is a surname most prominently associated with Franz Boas, the pioneering anthropologist often regarded as the father of American anthropology.
  • B. Gecko
    Gecko is Mozilla’s open-source web browser engine that powers the rendering and functionality of Firefox and several other applications.
  • C. Colubrina
    Colubrina is a genus of flowering shrubs and small trees known for their hard wood and occurrence in tropical and subtropical regions.
  • D. Pongo
    Pongo is the genus of great apes commonly known as orangutans, native to the rainforests of Borneo and Sumatra.
  • E. Miga
    Miga is one of the official mascots of the 2010 Winter Olympics in Vancouver, depicted as a playful sea bear inspired by orcas and First Nations legends.
  • 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_69a885ffc5ec819091afa325d5f9611c completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9098e245c8190b0169b648434aa49 completed March 5, 2026, 4:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad51c751308190a89f6462ee365418 completed March 8, 2026, 10:39 a.m.
NEDg Description generation batch_69ad52f6b15c819097d4e56884e31600 completed March 8, 2026, 10:44 a.m.
NED2 Entity disambiguation (via description) batch_69ad53536a108190b97b7017ad567ad5 completed March 8, 2026, 10:45 a.m.
Created at: March 4, 2026, 7:28 p.m.