Triple
T7888135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosa Taikon |
E183155
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Rosa Taikon |
E183155
|
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: Rosa Taikon | Statement: [Rosa Taikon, name, Rosa Taikon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosa Taikon Context triple: [Rosa Taikon, name, Rosa Taikon]
-
A.
Rosa Taikon
chosen
Rosa Taikon was a renowned Swedish Romani silversmith and civil rights activist known for her intricate jewelry and her prominent role in advocating for Roma rights in Sweden.
-
B.
Rina
Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
-
C.
Geisa
Geisa is a small historic town in the state of Thuringia in central Germany, near the former inner-German border.
-
D.
Risoul
Risoul is a French alpine ski resort and commune known for its extensive slopes and scenic location in the southern Alps.
-
E.
Rittō
Rittō is a city in Shiga Prefecture, Japan, known for its residential communities and proximity to the Kyoto–Osaka 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_69ca828af6e48190a06ee7010d8f0e64 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39ea8d1c81908ef99569e0cf00b7 |
completed | March 31, 2026, 3:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdfb5d7e08190a04dc9d3dc35a0e6 |
completed | March 31, 2026, 2:52 p.m. |
Created at: March 30, 2026, 4:59 p.m.