Triple
T17092186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rusticucci family |
E414751
|
entity |
| Predicate | influencedUrbanToponymy |
P98810
|
FINISHED |
| Object | area once called Piazza Rusticucci |
—
|
LITERAL 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: area once called Piazza Rusticucci | Statement: [Rusticucci family, influencedUrbanToponymy, area once called Piazza Rusticucci]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedUrbanToponymy Context triple: [Rusticucci family, influencedUrbanToponymy, area once called Piazza Rusticucci]
-
A.
influenceOnToponymy
chosen
Indicates that one entity has affected or shaped the naming, form, or development of place names associated with another entity.
-
B.
hasToponymicMotivation
Indicates that something is motivated, derived, or named based on a place name (toponym).
-
C.
usedAsToponymicBy
Indicates that one entity is employed as a place-based surname or name-forming element for another entity.
-
D.
inspiredToponymCountry
Indicates that a country’s name was inspired by, derived from, or otherwise based on a particular toponym (place name).
-
E.
cityOfInfluence
Indicates the city that significantly shapes, impacts, or exerts influence over a given entity.
- F. None of above.
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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfabf548190a0d37bab3d4ef2fa |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.