Triple
T17506164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baia dei Turchi |
E426320
|
entity |
| Predicate | hasNaturalCharacter |
P119605
|
FINISHED |
| Object | largely unbuilt shoreline |
—
|
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: largely unbuilt shoreline | Statement: [Baia dei Turchi, hasNaturalCharacter, largely unbuilt shoreline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaturalCharacter Context triple: [Baia dei Turchi, hasNaturalCharacter, largely unbuilt shoreline]
-
A.
hasNaturalForm
chosen
Indicates that an entity possesses an inherent, unaltered, or naturally occurring form or state.
-
B.
hasHumanCharacters
Indicates that the subject includes or features characters that are human beings.
-
C.
hasLanguageCharacter
Indicates that an entity uses, contains, or is associated with a specific written or symbolic character from a language.
-
D.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
-
E.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
- 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452159c28819084b2cba4313ddf28 |
completed | April 19, 2026, 3:55 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.