Triple
T30871334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucius Caecilius Metellus Calvus |
E786347
|
entity |
| Predicate | hasNomenGentilicium |
P180425
|
FINISHED |
| Object | Caecilius |
—
|
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: Caecilius | Statement: [Lucius Caecilius Metellus Calvus, hasNomenGentilicium, Caecilius]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNomenGentilicium Context triple: [Lucius Caecilius Metellus Calvus, hasNomenGentilicium, Caecilius]
-
A.
hasGentilicForm
chosen
Indicates that one term is the gentilic (demonym or adjectival form denoting origin or affiliation) derived from or associated with another term.
-
B.
hasLatinName
Indicates that an entity is associated with a specific Latin (scientific) name.
-
C.
hasLatinizedName
Indicates that an entity is associated with a version of its name that has been converted into Latin form or spelling.
-
D.
hasDemonym
Indicates that one entity is the term (demonym) used to refer to the inhabitants or natives of another entity (typically a place).
-
E.
memberHasCognomen
Indicates that a member is associated with or bears a specific cognomen (surname or additional family 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_69f224b9df2c819086f55f8bcf7f382e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0067cde0f08190b2cd93af5f00d519 |
completed | May 10, 2026, 11:11 a.m. |
| PD | Predicate disambiguation | batch_6a0065820c8c8190994734433c64a30a |
completed | May 10, 2026, 11:01 a.m. |
Created at: April 29, 2026, 8:48 p.m.