Triple
T23649568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rotomagus |
E584130
|
entity |
| Predicate | locatedInModernDepartment |
P153033
|
FINISHED |
| Object | Seine-Maritime |
—
|
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: Seine-Maritime | Statement: [Rotomagus, locatedInModernDepartment, Seine-Maritime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInModernDepartment Context triple: [Rotomagus, locatedInModernDepartment, Seine-Maritime]
-
A.
partlyCorrespondsToModernDepartment
Indicates that an entity’s historical or former administrative area overlaps only in part with the boundaries of a specified modern department.
-
B.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
-
C.
worksInDepartment
Indicates that an entity is employed in and performs their job duties within a particular department.
-
D.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
-
E.
largestModernDepartment
Indicates that one entity is the largest modern department (by size, scope, or another defined metric) in relation to a specified set or context.
- 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_69e248fefafc81909656921192f30e80 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b2885b408190a43dfed93309a4d6 |
completed | April 29, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69f118d7903c8190bb590a71771e93af |
completed | April 28, 2026, 8:30 p.m. |
| PDg | Predicate description generation | batch_69f1233300bc8190ac1639bdca1d7d99 |
completed | April 28, 2026, 9:14 p.m. |
Created at: April 17, 2026, 6:49 p.m.