Triple
T3066377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea with Mussolini |
E62112
|
entity |
| Predicate | setInLocation |
P40
|
FINISHED |
| Object | Florence |
E26762
|
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: Florence | Statement: [Tea with Mussolini, setInLocation, Florence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Florence Context triple: [Tea with Mussolini, setInLocation, Florence]
-
A.
Florence
Florence is a city in northwestern Alabama known as part of the Muscle Shoals metropolitan area and for its rich musical and cultural heritage.
-
B.
Florence
Florence is the birth name of Elizabeth Arden, the pioneering Canadian-American businesswoman who founded the iconic Elizabeth Arden cosmetics brand.
-
C.
Florence
chosen
Florence is a historic Italian city renowned as the cradle of the Renaissance, celebrated for its art, architecture, and cultural influence.
-
D.
Florence
Florence is a small coastal city in western Oregon known for its scenic beaches, sand dunes, and historic Old Town along the Siuslaw River.
-
E.
Pisa
Pisa is a historic Italian city in Tuscany best known for its iconic Leaning Tower and as a significant center of medieval trade, learning, and architecture.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fd87308190918e7b616f033faa |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef16cf2881908265dfe8a1e3424d |
completed | March 11, 2026, 10:39 p.m. |
Created at: March 8, 2026, 3:02 p.m.