Triple
T5883019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mtume |
E130794
|
entity |
| Predicate | stageName |
P7872
|
FINISHED |
| Object | Mtume |
E130794
|
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: Mtume | Statement: [Mtume, stageName, Mtume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mtume Context triple: [Mtume, stageName, Mtume]
-
A.
Mtume
chosen
Mtume was an American R&B and jazz-funk musician, songwriter, and producer best known for leading the band Mtume and co-writing the hit song "Juicy Fruit."
-
B.
Mukuzani
Mukuzani is a renowned Georgian red wine appellation known for producing dry, oak-aged wines from the Saperavi grape in the Kakheti region.
-
C.
Mzilikazi
Mzilikazi was a 19th-century Southern African king who founded the Ndebele (Matabele) nation and led its migration to what is now Zimbabwe.
-
D.
Mpulungu
Mpulungu is a Zambian port town that serves as the country’s main access point to Lake Tanganyika and a hub for regional fishing and trade.
-
E.
Zuma
Zuma is a playful chocolate Labrador pup from PAW Patrol who serves as the team's water rescue and aquatic expert.
- 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_69c0085628dc8190b334c1b44c067efc |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03675747c81908936405c27c2719b |
completed | March 22, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b13330c88190b10f33843f10f51f |
completed | March 23, 2026, 3:19 a.m. |
Created at: March 22, 2026, 3:57 p.m.