Triple
T33413387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eclanum |
E855647
|
entity |
| Predicate | languageUsedHistorical |
P1434
|
FINISHED |
| Object | Latin |
—
|
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 | Statement: [Eclanum, languageUsedHistorical, Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedHistorical Context triple: [Eclanum, languageUsedHistorical, Latin]
-
A.
historicalLanguage
Indicates that one language is a historical or earlier form/ancestor of another language.
-
B.
languageOfHistoricalRecord
Indicates the language in which a given historical record is written or recorded.
-
C.
historicallySpokenIn
chosen
Indicates that a language was used for spoken communication in a particular place or region during a past historical period.
-
D.
languageOfHistoricalActivity
Indicates the language in which a historical activity, event, or action was conducted or recorded.
-
E.
historicalLanguageOfBearers
Indicates that the specified language is historically spoken or used by the bearers of a given name, title, or designation.
- 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_69f3496f04a08190804e56ac5098b8e4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a001adc8c108190ab3a43f6415e2be3 |
completed | May 10, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_6a001a290330819097c2c3123f9014b4 |
completed | May 10, 2026, 5:39 a.m. |
Created at: May 1, 2026, 1:36 a.m.