Triple
T10974591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don John |
E259334
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Messina |
E83961
|
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: Messina | Statement: [Don John, setting, Messina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Messina Context triple: [Don John, setting, Messina]
-
A.
Messina
Messina is an Italian surname most notably associated with various public figures in politics, entertainment, and sports.
-
B.
Messina
chosen
Messina is a major port city in northeastern Sicily, Italy, located on the Strait of Messina opposite mainland Calabria.
-
C.
Trapani
Trapani is a coastal city in western Sicily, Italy, known for its historic port, salt pans, and proximity to the Egadi Islands.
-
D.
Palermo
Palermo is the historic capital of Sicily, renowned for its rich multicultural heritage, including a significant medieval Jewish presence, and its blend of Arab-Norman architecture, vibrant markets, and coastal setting.
-
E.
Palermo
Palermo is a 90 nm, low-power, budget-oriented core used in AMD's Sempron line of processors.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d771f3794c8190b1992b4695139b62 |
completed | April 9, 2026, 9:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4acc9d60c819084e342076fe682fc |
completed | April 19, 2026, 10:22 a.m. |
Created at: April 8, 2026, 9:24 p.m.