Triple
T22059721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonardo’s workshop |
E545119
|
entity |
| Predicate | locationPeriod |
P7934
|
FINISHED |
| Object | Florence |
—
|
NE NERFINISHED |
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: [Leonardo’s workshop, locationPeriod, Florence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Florence Context triple: [Leonardo’s workshop, locationPeriod, Florence]
-
A.
Florence
chosen
Florence is a historic Italian city renowned as the cradle of the Renaissance, celebrated for its art, architecture, and cultural influence.
-
B.
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.
-
C.
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.
-
D.
Florence
Florence is a neighborhood in South Los Angeles known for its dense urban character, diverse working-class community, and proximity to major transportation corridors.
-
E.
Florence
Florence is a kind, sensible young girl and one of the main human characters in the classic stop-motion children's television series "The Magic Roundabout."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3377c48190890c17407b9527d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1285ac9608190ab4f89d4ee7350d0 |
completed | April 28, 2026, 9:36 p.m. |
Created at: April 16, 2026, 8:27 p.m.