Triple
T1644190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umbria |
E35541
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Assisi |
E76823
|
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: Assisi | Statement: [Umbria, containsCity, Assisi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assisi Context triple: [Umbria, containsCity, Assisi]
-
A.
Assisi
chosen
Assisi is an Italian hill town in Umbria renowned as the birthplace of St. Francis and a major center of Christian pilgrimage.
-
B.
Perugia
Perugia is a historic hilltop city in central Italy, renowned for its Etruscan heritage, medieval architecture, and vibrant cultural and university life.
-
C.
Orvieto
Orvieto is a historic hilltop city in Umbria, Italy, renowned for its dramatic cliffside setting and magnificent Gothic cathedral.
-
D.
Gubbio
Gubbio is a historic medieval town in the Umbria region of central Italy, known for its well-preserved stone architecture and traditional festivals.
-
E.
Viterbo
Viterbo is a historic city in central Italy known for its well-preserved medieval center, ancient thermal baths, and role as a papal residence in the 13th century.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa622e9b08819094960b2329c6e7e6 |
completed | March 6, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeac40c608190800da8b029ef065a |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 4, 2026, 7:28 p.m.