Triple
T33540449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Signore di Milano |
E859054
|
entity |
| Predicate | hasAssociatedCityStateType |
P121869
|
FINISHED |
| Object | northern Italian city‑state |
—
|
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: northern Italian city‑state | Statement: [Signore di Milano, hasAssociatedCityStateType, northern Italian city‑state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedCityStateType Context triple: [Signore di Milano, hasAssociatedCityStateType, northern Italian city‑state]
-
A.
hasAssociatedCity
Indicates that one entity is linked or related to a specific city, typically as its location, base, or primary area of association.
-
B.
belongsToCityState
Indicates that an entity is located within or associated with a specific city and state jurisdiction.
-
C.
belongsToCityType
chosen
Indicates that one entity is classified under, or associated with, a particular type or category of city.
-
D.
associatedCityState
Indicates a relationship where a city is linked to the state with which it is formally or contextually connected.
-
E.
containsCityState
Indicates that one entity includes or encompasses a specific city and state within its scope or boundaries.
- 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_69f3497a5be08190a39b12736899e034 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 1, 2026, 1:39 a.m.