Triple
T4275645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vapor |
E97041
|
entity |
| Predicate | supportsDatabase |
P11254
|
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: [Vapor, supportsDatabase, MongoDB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MongoDB Context triple: [Vapor, supportsDatabase, MongoDB]
-
A.
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.
-
B.
MongoDB database
chosen
MongoDB database is a popular open-source NoSQL document-oriented database designed for scalability, flexibility, and high performance in modern applications.
-
C.
10gen
10gen is the original company behind the development of the MongoDB NoSQL database, later renamed MongoDB Inc.
-
D.
MongoDB Inc.
MongoDB Inc. is a software company best known for developing the popular open-source NoSQL document database MongoDB, widely used for scalable, modern application development.
-
E.
Amazon DocumentDB
Amazon DocumentDB is a fully managed, scalable document database service from AWS designed to be compatible with MongoDB workloads and optimized for performance, durability, and security in the cloud.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501c35688190a7d15d904f15f968 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b0b2ec819090ccf042917ae207 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.