Triple
T35293713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legnanesi |
E1019297
|
entity |
| Predicate | gentilicSingularMasculine |
P180425
|
FINISHED |
| Object | Legnanese |
—
|
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: Legnanese | Statement: [Legnanesi, gentilicSingularMasculine, Legnanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gentilicSingularMasculine Context triple: [Legnanesi, gentilicSingularMasculine, Legnanese]
-
A.
gentilicium
Indicates that an entity’s family or clan affiliation is expressed through a gentilic (family) name.
-
B.
hasGentilicForm
chosen
Indicates that one term is the gentilic (demonym or adjectival form denoting origin or affiliation) derived from or associated with another term.
-
C.
usesGentilicFor
Indicates that one entity refers to another using a gentilic (a demonym or term denoting origin, nationality, or regional affiliation).
-
D.
gentilicLanguage
Indicates that a language is associated with or derived from a particular gentilic (demonym) for a people or place.
-
E.
correspondingMasculinePatronymic
Indicates that one name is the masculine patronymic form that corresponds to another base name or related name.
- 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_69f76de7eedc8190a3bdc64ebbc05b42 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.