Triple
T14993370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miro |
E373892
|
entity |
| Predicate | hasSpelling |
P457
|
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: [Miro, hasSpelling, Miro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miro Context triple: [Miro, hasSpelling, Miro]
-
A.
Miro
chosen
Miro is a common Finnish given name, often used for males and sometimes derived from longer names like Miroslav.
-
B.
Miro the Young
Miro the Young was a medieval Catalan nobleman and count, known as the son and successor of Wilfred the Hairy in parts of the Catalan counties.
-
C.
Mekiro
Mekiro is one of the smaller islands that form part of the remote Gambier Islands archipelago in French Polynesia.
-
D.
Mireo
Mireo is a family of modern regional and commuter trains developed by Siemens Mobility, known for their energy efficiency, modular design, and suitability for short- to medium-distance rail services.
-
E.
Miki
Miki is a city located in Japan’s Kagawa Prefecture on the island of Shikoku.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded716ebb481908224d2d4f7561b03 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe969842848190a030db797c851fed |
completed | May 9, 2026, 2:06 a.m. |
Created at: April 10, 2026, 2:53 a.m.