Triple
T28772576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris (department) |
E726449
|
entity |
| Predicate | isMostPopulousDepartmentInFrance |
P153601
|
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: [Paris (department), isMostPopulousDepartmentInFrance, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isMostPopulousDepartmentInFrance Context triple: [Paris (department), isMostPopulousDepartmentInFrance, true]
-
A.
isMostPopulousDepartmentOf
chosen
Indicates that one administrative department has the largest population among all departments within a specified region or country.
-
B.
hasPopulationRankInDepartment
Indicates the relative position of an entity’s population size compared to other entities within the same department.
-
C.
mainFrenchBorderDepartments
Indicates that the referenced departments are the primary administrative regions of France that share a land border with neighboring countries.
-
D.
cityLocatedInDepartment
Indicates that a city is geographically and administratively situated within a specific department.
-
E.
prefectureOfDepartment
Indicates that a given prefecture administers or is the capital authority of a specified department.
- 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_69f03199997c8190b6ae43fb19312443 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6617ba4a88190bfc5c305acb4f93f |
completed | May 2, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69f660f082508190a95a7888ad66cb2e |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 28, 2026, 6:16 a.m.