Triple
T709209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Etruria |
E14168
|
entity |
| Predicate | modernTerritoryIncludes |
P8991
|
FINISHED |
| Object | Umbria |
E35541
|
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: Umbria | Statement: [Etruria, modernTerritoryIncludes, Umbria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Umbria Context triple: [Etruria, modernTerritoryIncludes, Umbria]
-
A.
Umbria
chosen
Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
-
B.
Abruzzo
Abruzzo is a central Italian region known for its rugged Apennine mountains, national parks, and Adriatic Sea coastline.
-
C.
Emilia-Romagna
Emilia-Romagna is a region in northern Italy known for its rich culinary traditions, historic cities, and strong industrial and agricultural economy.
-
D.
Tuscany
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.
-
E.
Molise
Molise is a small, predominantly rural region in southern Italy known for its mountainous landscapes, traditional agriculture, and relatively low population density.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a55b63988190837e71fcdf3e39a6 |
completed | March 1, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac118aa3c48190be7ca68d17457ea8 |
completed | March 7, 2026, 11:52 a.m. |
Created at: March 1, 2026, 7:36 p.m.