Triple
T16978487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bikita Minerals |
E411877
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bikita |
E411877
|
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: Bikita | Statement: [Bikita Minerals, locatedIn, Bikita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bikita Context triple: [Bikita Minerals, locatedIn, Bikita]
-
A.
Bikita
chosen
Bikita is a rural district and settlement in southeastern Zimbabwe known for its lithium-rich mineral deposits and agricultural communities.
-
B.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
C.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
-
D.
Sanjo
Sanjo was a Japanese noblewoman best known as the principal wife of the Sengoku-period warlord Takeda Shingen.
-
E.
Kitanagoya
Kitanagoya is a city in central Japan known as a residential and commercial suburb within the Nagoya metropolitan area.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d185a9408190a991bf8a1ef694f0 |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d477f7ec81909f1f0243004c9050 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:32 a.m.