Triple
T4325686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flask-Admin |
E96629
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object |
MongoEngine
MongoEngine is a popular Object-Document Mapper (ODM) for working with MongoDB in Python applications.
|
E430986
|
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: MongoEngine | Statement: [Flask-Admin, supports, MongoEngine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MongoEngine Context triple: [Flask-Admin, supports, MongoEngine]
-
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
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 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.
-
E.
Flask-SQLAlchemy
Flask-SQLAlchemy is a popular Flask extension that integrates the SQLAlchemy ORM with Flask applications to simplify database configuration and usage.
- 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: MongoEngine Triple: [Flask-Admin, supports, MongoEngine]
Generated description
MongoEngine is a popular Object-Document Mapper (ODM) for working with MongoDB in Python applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MongoEngine Target entity description: MongoEngine is a popular Object-Document Mapper (ODM) for working with MongoDB in Python applications.
-
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
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 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.
-
E.
Flask-SQLAlchemy
Flask-SQLAlchemy is a popular Flask extension that integrates the SQLAlchemy ORM with Flask applications to simplify database configuration and usage.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3512ec18481908a7b5c29b3902b53 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d09861a4819086a88bb42a8ea2e4 |
completed | March 14, 2026, 9:18 p.m. |
| NEDg | Description generation | batch_69b5d11a30a08190b9f58fadd2415559 |
completed | March 14, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5d194975481908b029ab106223c6c |
completed | March 14, 2026, 9:22 p.m. |
Created at: March 12, 2026, 11:13 p.m.