Triple
T12607474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gravedona |
E301019
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Dongo |
E54844
|
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: Dongo | Statement: [Gravedona, nearbyTown, Dongo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongo Context triple: [Gravedona, nearbyTown, Dongo]
-
A.
Dongo
chosen
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
-
B.
Dondo
Dondo is an Austronesian language spoken in Central Sulawesi, Indonesia, belonging to the Tomini–Tolitoli subgroup.
-
C.
Kabuna
Kabuna is a small village located on the atoll of Tabiteuea in the island nation of Kiribati in the central Pacific Ocean.
-
D.
Machar
Machar is a small rural township in Ontario, Canada, known for its forests, lakes, and low-density residential and agricultural character.
-
E.
Wamba
Wamba is a town and administrative local government area in Nasarawa State, central Nigeria, known for its diverse ethnic communities and agricultural activities.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954e90efc81909951dbe698afa851 |
completed | April 10, 2026, 7:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f66869c0b08190b13bcebbe354cd98 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:11 p.m.