Triple
T14674141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maiella |
E344593
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Abruzzo |
E69244
|
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: Abruzzo | Statement: [Maiella, region, Abruzzo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abruzzo Context triple: [Maiella, region, Abruzzo]
-
A.
Abruzzo
chosen
Abruzzo is a central Italian region known for its rugged Apennine mountains, national parks, and Adriatic Sea coastline.
-
B.
D’Abruzzo
D’Abruzzo is an Italian surname associated with actor Robert Alda, reflecting his family’s Abruzzese heritage.
-
C.
Molise
Molise is a small, predominantly rural region in southern Italy known for its mountainous landscapes, traditional agriculture, and relatively low population density.
-
D.
La Marche
La Marche is a historic province in central France known for its rural landscapes and role as a frontier region between major medieval territories.
-
E.
Umbria
Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb55064cc8190b9669d0b2da61825 |
completed | April 14, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cde041081908ae2f2c75a9d5eb2 |
completed | May 8, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:27 a.m.