Triple
T11923848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meylan |
E283730
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Torgiano |
E379812
|
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: Torgiano | Statement: [Meylan, hasTwinTown, Torgiano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torgiano Context triple: [Meylan, hasTwinTown, Torgiano]
-
A.
Torgiano
chosen
Torgiano is a historic town in the Umbria region of central Italy, known for its wine production and scenic riverside setting.
-
B.
San Terenzo
San Terenzo is a small coastal village in Liguria, Italy, known for its scenic beaches, historic castle, and views across the Gulf of La Spezia.
-
C.
Todi
Todi is a historic hilltop town in central Italy known for its medieval architecture, scenic views over the Tiber Valley, and well-preserved city walls and churches.
-
D.
Montefiascone
Montefiascone is a historic hilltop town in Italy’s Lazio region, known for its scenic views over Lake Bolsena and its production of the Est! Est!! Est!!! white wine.
-
E.
Sinalunga
Sinalunga is a historic town in Tuscany, central Italy, known for its medieval architecture and scenic location within the rolling hills of the Val di Chiana.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8e2fc648190a446c1917db1c7d9 |
completed | April 10, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471a856208190bb88254090c03ed0 |
completed | May 1, 2026, 9:26 a.m. |
Created at: April 8, 2026, 9:45 p.m.