Triple
T4254779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miroslav |
E95945
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Miro |
E373892
|
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: Miro | Statement: [Miroslav, hasDiminutive, Miro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miro Context triple: [Miroslav, hasDiminutive, Miro]
-
A.
Miro
chosen
Miro is a common Finnish given name, often used for males and sometimes derived from longer names like Miroslav.
-
B.
Miki
Miki is a city in Japan located within Hyogo Prefecture, known for its traditional hardware industry and historical sites.
-
C.
Miran
Miran was the son of Mir Jafar, the controversial Nawab of Bengal installed by the British East India Company in the mid-18th century.
-
D.
Mechta
Mechta is the alternative name for Luna 1, the Soviet spacecraft that became the first human-made object to reach the vicinity of the Moon and enter a heliocentric orbit.
-
E.
Mirik
Mirik is a small hill town and popular tourist destination in the Darjeeling district of West Bengal, India, known for its scenic lake, tea gardens, and pleasant climate.
- 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_69b3453f759881909b91f01a1e82c036 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34ec036e8819087d8585170707545 |
completed | March 12, 2026, 11:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a887703c81909a40f23f83154b8c |
completed | March 14, 2026, 6:27 p.m. |
Created at: March 12, 2026, 11:06 p.m.