Triple
T8585695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buganda |
E203300
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Mengo |
E432504
|
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: Mengo | Statement: [Buganda, capital, Mengo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mengo Context triple: [Buganda, capital, Mengo]
-
A.
Mengo
chosen
Mengo is a historic hill and neighborhood in Kampala, Uganda, that served as the traditional political and cultural center of the Kingdom of Buganda.
-
B.
Mantoro
Mantoro is a traditional Japanese lantern festival held at Kasuga Taisha Shrine in Nara, where thousands of stone and hanging lanterns are lit to create a spectacular nighttime illumination.
-
C.
Munefusa
Munefusa is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and travel writer.
-
D.
Maseo
Maseo is the DJ and producer of the influential hip hop group De La Soul, known for his role in shaping their innovative, sample-rich sound.
-
E.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
- 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_69ca8329bb7c8190a63c643730839103 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc457ab8b08190a53c730417288deb |
completed | March 31, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea89e4658819090cc6e94e934670b |
completed | April 2, 2026, 5:34 p.m. |
Created at: March 30, 2026, 6:22 p.m.