Triple
T8542403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rozvi state |
E202228
|
entity |
| Predicate | rulerTitle |
P593
|
FINISHED |
| Object | Mambo |
E741201
|
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: Mambo | Statement: [Rozvi state, rulerTitle, Mambo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mambo Context triple: [Rozvi state, rulerTitle, Mambo]
-
A.
Mambo
Mambo is an open-source content management system that was widely used in the early 2000s for building dynamic websites and later served as the codebase origin for Joomla!.
-
B.
Mambo
chosen
Mambo was the royal title used for the supreme ruler of the Rozvi Empire in what is now Zimbabwe.
-
C.
Mambo!
Mambo! is a 1950s exotica and Latin-influenced studio album by Peruvian soprano Yma Sumac, showcasing her extraordinary multi-octave vocal range.
-
D.
Mambo Kingz
Mambo Kingz is a Latin music production duo known for crafting reggaeton and urban hits for top artists in the Spanish-speaking music scene.
-
E.
Mambo Mouth
Mambo Mouth is a one-man off-Broadway stage show by John Leguizamo in which he portrays multiple Latino characters in a fast-paced, comedic performance.
- 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_69ca832461e88190a654c5e44e233aa8 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6e26be48190b10bc62fad178dad |
completed | March 31, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce891a1fac8190bcae4063b24c760a |
completed | April 2, 2026, 3:19 p.m. |
Created at: March 30, 2026, 6:18 p.m.