Triple
T1296617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Duke of Tuscany |
E27666
|
entity |
| Predicate | locatedInHistoricalRegion |
P915
|
FINISHED |
| Object | Tuscany |
E34826
|
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: Tuscany | Statement: [Grand Duke of Tuscany, locatedInHistoricalRegion, Tuscany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuscany Context triple: [Grand Duke of Tuscany, locatedInHistoricalRegion, Tuscany]
-
A.
Tuscany
chosen
Tuscany is a central Italian region renowned for its rolling landscapes, historic cities like Florence and Siena, and its pivotal role in art, culture, and the birth of the Renaissance.
-
B.
Umbria
Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
-
C.
Liguria
Liguria is a coastal region in northwestern Italy known for its picturesque Riviera, including the Cinque Terre and the city of Genoa.
-
D.
Senigallia
Senigallia is a historic coastal town in Italy’s Marche region, known for its Adriatic seaside resort, Renaissance heritage, and well-preserved old town.
-
E.
Emilia-Romagna
Emilia-Romagna is a region in northern Italy known for its rich culinary traditions, historic cities, and strong industrial and agricultural economy.
- 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0f591ec819084f01f518c332880 |
completed | March 1, 2026, 10:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf379d048190924ad8dfa9ac5e7a |
completed | March 8, 2026, 6:25 p.m. |
Created at: March 1, 2026, 7:51 p.m.