Triple
T7217858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keimyung University |
E150180
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
KMU
KMU is the abbreviation commonly used for Keimyung University, a private Christian university located in Daegu, South Korea.
|
E649955
|
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: KMU | Statement: [Keimyung University, shortName, KMU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KMU Context triple: [Keimyung University, shortName, KMU]
-
A.
KSMF
KSMF is the ICAO airport code for Sacramento International Airport, a major commercial airport serving California’s capital region.
-
B.
KMLU
KMLU is the ICAO airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
-
C.
KMCI
KMCI is the ICAO code for Kansas City International Airport, a major commercial airport serving the Kansas City metropolitan area in Missouri, USA.
-
D.
KRCU
KRCU is a public radio station affiliated with Southeast Missouri State University that provides news, music, and cultural programming to the surrounding region.
-
E.
KCSM
KCSM is the Mexican subsidiary of the Kansas City Southern railway company, operating freight rail services across key industrial and cross-border corridors in Mexico.
- 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: KMU Triple: [Keimyung University, shortName, KMU]
Generated description
KMU is the abbreviation commonly used for Keimyung University, a private Christian university located in Daegu, South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KMU Target entity description: KMU is the abbreviation commonly used for Keimyung University, a private Christian university located in Daegu, South Korea.
-
A.
KSMF
KSMF is the ICAO airport code for Sacramento International Airport, a major commercial airport serving California’s capital region.
-
B.
KMLU
KMLU is the ICAO airport code for Monroe Regional Airport, a public airport serving Monroe, Louisiana, in the United States.
-
C.
KMCI
KMCI is the ICAO code for Kansas City International Airport, a major commercial airport serving the Kansas City metropolitan area in Missouri, USA.
-
D.
KRCU
KRCU is a public radio station affiliated with Southeast Missouri State University that provides news, music, and cultural programming to the surrounding region.
-
E.
KCSM
KCSM is the Mexican subsidiary of the Kansas City Southern railway company, operating freight rail services across key industrial and cross-border corridors in Mexico.
- 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_69c687effb44819092b95d07d0368c9f |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e99170d88190b1aef326a7d81134 |
completed | March 27, 2026, 8:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbfb46388190992cc98039e71748 |
completed | March 28, 2026, 12:39 p.m. |
| NEDg | Description generation | batch_69c7cce6a290819096ff68333cd3a3cf |
completed | March 28, 2026, 12:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7cd9966e481909eb3c23bb59777d9 |
completed | March 28, 2026, 12:46 p.m. |
Created at: March 27, 2026, 2:53 p.m.