Triple
T10713609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazakh National Medical University |
E252604
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
KazNMU
KazNMU is a leading medical university in Kazakhstan known for training healthcare professionals and conducting medical research.
|
E881075
|
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: KazNMU | Statement: [Kazakh National Medical University, shortName, KazNMU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KazNMU Context triple: [Kazakh National Medical University, shortName, KazNMU]
-
A.
KazNU
KazNU is a leading public research university in Almaty, Kazakhstan, recognized as one of the country’s oldest and most prestigious higher education institutions.
-
B.
KAZ
KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
-
C.
Kaz
Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
-
D.
Kaz
Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
-
E.
Kaz
Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
- 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: KazNMU Triple: [Kazakh National Medical University, shortName, KazNMU]
Generated description
KazNMU is a leading medical university in Kazakhstan known for training healthcare professionals and conducting medical research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KazNMU Target entity description: KazNMU is a leading medical university in Kazakhstan known for training healthcare professionals and conducting medical research.
-
A.
KazNU
KazNU is a leading public research university in Almaty, Kazakhstan, recognized as one of the country’s oldest and most prestigious higher education institutions.
-
B.
KAZ
KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
-
C.
Kaz
Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
-
D.
Kaz
Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
-
E.
Kaz
Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fe54465081909640f6d7a2314fcb |
completed | April 9, 2026, 1:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d99917df2c819099be2a9b9c4a2ce7 |
completed | April 11, 2026, 12:43 a.m. |
| NEDg | Description generation | batch_69d99e86e198819080f71400295a31e7 |
completed | April 11, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69dadccd1d7081908ae53b97d2c2a6cb |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 8, 2026, 9:13 p.m.