Triple
T721006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Picene |
E14614
|
entity |
| Predicate | hasKnownGrammar |
P18535
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [North Picene, hasKnownGrammar, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKnownGrammar Context triple: [North Picene, hasKnownGrammar, false]
-
A.
hasDistinctGrammar
Indicates that the subject’s grammar system is different in structure or rules from that of the object.
-
B.
hasGrammarDifferenceFrom
Indicates that two linguistic items differ from each other in their grammatical form, structure, or rules of usage.
-
C.
hasInfluentialGrammarian
Indicates that an entity is associated with, or characterized by, a grammarian who has significant influence or authority in matters of grammar.
-
D.
hasLinguisticElement
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
-
E.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
- F. None of above. chosen
Provenance (4 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58fa41c819082de2cc4e0cb2943 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f513608190b716b939d574c292 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a57267c481909790a1fda3fced08 |
completed | March 1, 2026, 8:45 p.m. |
Created at: March 1, 2026, 7:37 p.m.