Triple
T30112071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Department of Paris |
E765303
|
entity |
| Predicate | hasPopulationRankInFrance |
P12637
|
FINISHED |
| Object | most populous commune of France |
—
|
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: most populous commune of France | Statement: [Department of Paris, hasPopulationRankInFrance, most populous commune of France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInFrance Context triple: [Department of Paris, hasPopulationRankInFrance, most populous commune of France]
-
A.
populationRankInFrance
chosen
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
B.
economicRankInFrance
Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
-
C.
hasPopulationRankInDepartment
Indicates the relative position of an entity’s population size compared to other entities within the same department.
-
D.
collectionRankInFrance
Indicates the position or level of a collection within a ranking specific to France.
-
E.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69f22475ad7c8190be7f9541044a0bbb |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd884cb2b48190b6acd473430d9e19 |
completed | May 8, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69fd8709ca208190a8bab836f0156af5 |
completed | May 8, 2026, 6:47 a.m. |
Created at: April 29, 2026, 7:10 p.m.