Triple
T15108331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MongoDB Inc. |
E360845
|
entity |
| Predicate | product |
P490
|
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: [MongoDB Inc., product, MongoDB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MongoDB Context triple: [MongoDB Inc., product, MongoDB]
-
A.
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.
-
B.
Mongo
Mongo is a major Bantu language spoken primarily in the Democratic Republic of the Congo by the Mongo people.
-
C.
Mongo
Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
-
D.
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.
-
E.
MongoDB database
chosen
MongoDB database is a popular open-source NoSQL document-oriented database designed for scalability, flexibility, and high performance in modern applications.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058af8988190977d998f85893836 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7e912ac8190bd0e0c9cdbbd0194 |
completed | May 9, 2026, 4:28 a.m. |
Created at: April 10, 2026, 3:05 a.m.