Triple
T4082770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caltanissetta |
E87515
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Enna |
E61423
|
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: Enna | Statement: [Caltanissetta, hasNearbyCity, Enna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Enna Context triple: [Caltanissetta, hasNearbyCity, Enna]
-
A.
Enna
chosen
Enna is a historic hilltop city in central Sicily, Italy, known for its elevated position and panoramic views over the island.
-
B.
Ennia
Ennia was a former Dutch insurance company that later became part of Aegon through a merger.
-
C.
Ennia
Ennia is a historical figure known as the founder of the ancient city of Aegon.
-
D.
Aricia
Aricia was an ancient town in the Alban Hills of Latium, historically significant as a cult center of Diana and a key stop on the Via Appia near Rome.
-
E.
Toffia
Toffia is a small historic hilltop town in the Lazio region of central Italy, known for its medieval architecture and scenic countryside setting.
- 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_69aed9435cf48190ad1da737c962d19d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc7933b481909bb3e02c6c04c8ee |
completed | March 9, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b562c6456081908cca823ebb13936a |
completed | March 14, 2026, 1:29 p.m. |
Created at: March 9, 2026, 3:39 p.m.