Triple
T10639497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claudius (nomen) |
E250683
|
entity |
| Predicate | grammaticalCaseSystem |
P95111
|
FINISHED |
| Object | Latin declension |
—
|
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: Latin declension | Statement: [Claudius (nomen), grammaticalCaseSystem, Latin declension]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalCaseSystem Context triple: [Claudius (nomen), grammaticalCaseSystem, Latin declension]
-
A.
numberOfGrammaticalCases
Indicates the relationship that specifies how many distinct grammatical cases a language or linguistic system possesses.
-
B.
hasNounClassSystem
Indicates that an entity possesses a grammatical system in which nouns are categorized into distinct classes that affect their agreement with other elements in the language.
-
C.
hasCaseForms
Indicates that an entity possesses multiple grammatical case variants or inflected forms associated with it.
-
D.
hasPronounSystem
Indicates that an entity possesses or employs a particular system or set of rules for using pronouns.
-
E.
grammaticalAlignment
Indicates how a language aligns core grammatical roles (such as subject, object, or agent, patient) in its case marking or agreement system.
- 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_69d6aa5a4c4881908f39be6efe5981e5 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfcbe5308190986bba438d19e852 |
completed | April 8, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:04 p.m.