Triple
T1128599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyrrhenian Sea |
E24776
|
entity |
| Predicate | countryBorder |
P224
|
FINISHED |
| Object | Italy |
E863
|
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: Italy | Statement: [Tyrrhenian Sea, countryBorder, Italy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Italy Context triple: [Tyrrhenian Sea, countryBorder, Italy]
-
A.
Italy
chosen
Italy is a Southern European country known for its influential history, art, cuisine, and role as a founding member of the European Union.
-
B.
Italo
Italo is a masculine Italian given name historically borne by notable figures in politics, aviation, literature, and the arts.
-
C.
ITA
ITA is a U.S. government agency within the Department of Commerce that promotes American exports, ensures fair trade, and supports U.S. businesses in the global marketplace.
-
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.
France and Italy
France and Italy are neighboring European countries that share a long Alpine border, rich cultural heritage, and significant historical, economic, and touristic ties.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbdea9b88190a88da718bf5c1897 |
completed | March 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5eab1e6481908c175e175ae4743a |
completed | March 7, 2026, 5:21 p.m. |
Created at: March 1, 2026, 7:44 p.m.