Triple
T9974414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alex Karras |
E196289
|
entity |
| Predicate | characterPortrayed |
P1507
|
FINISHED |
| Object |
Mongo
Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
|
E832348
|
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: Mongo | Statement: [Alex Karras, characterPortrayed, Mongo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mongo Context triple: [Alex Karras, characterPortrayed, Mongo]
-
A.
Mongo
Mongo is a major Bantu language spoken primarily in the Democratic Republic of the Congo by the Mongo people.
-
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 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.
-
D.
MongoDB database
MongoDB database is a popular open-source NoSQL document-oriented database designed for scalability, flexibility, and high performance in modern applications.
-
E.
10gen
10gen is the original company behind the development of the MongoDB NoSQL database, later renamed MongoDB Inc.
- 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: Mongo Triple: [Alex Karras, characterPortrayed, Mongo]
Generated description
Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mongo Target entity description: Mongo is the dim-witted but immensely strong henchman from the satirical Western comedy film "Blazing Saddles."
-
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.
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.
MongoDB database
MongoDB database is a popular open-source NoSQL document-oriented database designed for scalability, flexibility, and high performance in modern applications.
-
E.
10gen
10gen is the original company behind the development of the MongoDB NoSQL database, later renamed MongoDB Inc.
- 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_69ca82eea2b88190a0e511d21a31f386 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb849da448190b826cb8fbf2eeb89 |
completed | April 2, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23ddcc01c8190bb00ac13cbbbb1fb |
completed | April 5, 2026, 10:47 a.m. |
| NEDg | Description generation | batch_69d23eb2971c8190bcdbc31b4ef19816 |
completed | April 5, 2026, 10:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d240d7b7e881909183d7c33bd8cb5b |
completed | April 5, 2026, 11 a.m. |
Created at: March 30, 2026, 8:48 p.m.