Triple
T25945732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman province of Dacia |
E653833
|
entity |
| Predicate | hadRomanLegionsStationed |
P86507
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Roman province of Dacia, hadRomanLegionsStationed, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadRomanLegionsStationed Context triple: [Roman province of Dacia, hadRomanLegionsStationed, true]
-
A.
RomanLegionsPresent
chosen
Indicates that Roman military legions are present at or within the specified location or context.
-
B.
wasRomanMilitaryZoneSince
Indicates that a place or region has been designated and used as a Roman military zone starting from a specified point in time.
-
C.
hasRomanRemains
Indicates that the subject contains or is the location of physical remains or archaeological evidence from the Roman period.
-
D.
wasRomanFort
Indicates that the subject functioned as a Roman military fort at some point in time.
-
E.
wasRomanTown
Indicates that the subject entity functioned as a town or urban settlement during the period of the Roman Empire.
- 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_69e7ab40ac788190a771bc499eb1ae5f |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f60464d4988190bc39b78c8e418547 |
completed | May 2, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69f5aff889988190ad10bcf1a280f717 |
completed | May 2, 2026, 8:04 a.m. |
Created at: April 22, 2026, 8:43 a.m.