Triple
T18015429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MongoEngine |
E430986
|
entity |
| Predicate | dependsOn |
P100
|
FINISHED |
| Object |
PyMongo
PyMongo is the official Python driver for MongoDB, providing tools to connect to, query, and manage MongoDB databases from Python applications.
|
E1300950
|
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: PyMongo | Statement: [MongoEngine, dependsOn, PyMongo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyMongo Context triple: [MongoEngine, dependsOn, PyMongo]
-
A.
MongoEngine
MongoEngine is a popular Object-Document Mapper (ODM) for working with MongoDB in Python applications.
-
B.
Mongo
Mongo is the fictional alien planet ruled by the villainous Ming the Merciless in the Flash Gordon universe.
-
C.
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.
-
D.
Mongo
Mongo is a major Bantu language spoken primarily in the Democratic Republic of the Congo by the Mongo people.
-
E.
Mongo
Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
- 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: PyMongo Triple: [MongoEngine, dependsOn, PyMongo]
Generated description
PyMongo is the official Python driver for MongoDB, providing tools to connect to, query, and manage MongoDB databases from Python applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PyMongo Target entity description: PyMongo is the official Python driver for MongoDB, providing tools to connect to, query, and manage MongoDB databases from Python applications.
-
A.
MongoEngine
MongoEngine is a popular Object-Document Mapper (ODM) for working with MongoDB in Python applications.
-
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 a major Bantu language spoken primarily in the Democratic Republic of the Congo by the Mongo people.
-
D.
Mongo
Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
-
E.
Mongo
Mongo is the fictional alien planet ruled by the villainous Ming the Merciless in the Flash Gordon universe.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a034324daec8190a9bbec1ad80c70f9 |
completed | May 12, 2026, 3:11 p.m. |
| NEDg | Description generation | batch_6a0343dc91688190ae8e2f051cefef85 |
completed | May 12, 2026, 3:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0344b6f4e081908ff2fbc7bfa4c4e1 |
completed | May 12, 2026, 3:18 p.m. |
Created at: April 10, 2026, 10:24 a.m.