Triple
T17107015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Kato |
E415126
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
SMUG
SMUG (Sexual Minorities Uganda) is a Ugandan LGBTQ+ rights organization known for advocating for the protection and recognition of sexual and gender minorities in a highly hostile legal and social environment.
|
E1251477
|
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: SMUG | Statement: [David Kato, employer, SMUG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SMUG Context triple: [David Kato, employer, SMUG]
-
A.
SM
SM is the vehicle registration code used on license plates for the city of Sremska Mitrovica in Serbia.
-
B.
SMO
SMO is the IATA airport code for Santa Monica Airport, a general aviation facility located in Santa Monica, California.
-
C.
MUG
MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
-
D.
Smugglarkungen
Smugglarkungen is a Swedish film featuring actor Björn Andrésen, best known for his iconic role in "Death in Venice."
-
E.
Simogo
Simogo is an independent Swedish game development studio known for its stylish, narrative-driven titles that blend music, art, and innovative gameplay.
- 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: SMUG Triple: [David Kato, employer, SMUG]
Generated description
SMUG (Sexual Minorities Uganda) is a Ugandan LGBTQ+ rights organization known for advocating for the protection and recognition of sexual and gender minorities in a highly hostile legal and social environment.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SMUG Target entity description: SMUG (Sexual Minorities Uganda) is a Ugandan LGBTQ+ rights organization known for advocating for the protection and recognition of sexual and gender minorities in a highly hostile legal and social environment.
-
A.
SM
SM is the vehicle registration code used on license plates for the city of Sremska Mitrovica in Serbia.
-
B.
SMO
SMO is the IATA airport code for Santa Monica Airport, a general aviation facility located in Santa Monica, California.
-
C.
MUG
MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
-
D.
Smugglarkungen
Smugglarkungen is a Swedish film featuring actor Björn Andrésen, best known for his iconic role in "Death in Venice."
-
E.
Simogo
Simogo is an independent Swedish game development studio known for its stylish, narrative-driven titles that blend music, art, and innovative gameplay.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc280b0c8190b9e620b90e0d4b40 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a019540819083ce6100b24f8cfb |
completed | May 11, 2026, 2:08 a.m. |
| NEDg | Description generation | batch_6a013caf2fc48190912862b2e79d2d7f |
completed | May 11, 2026, 2:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a013d65bd5c8190b8355533d2d4ac40 |
completed | May 11, 2026, 2:22 a.m. |
Created at: April 10, 2026, 5:35 a.m.