Triple
T17664709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vitry-sur-Seine |
E440346
|
entity |
| Predicate | populationRankInValDeMarne |
P45844
|
FINISHED |
| Object | one of the most populous communes |
—
|
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: one of the most populous communes | Statement: [Vitry-sur-Seine, populationRankInValDeMarne, one of the most populous communes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRankInValDeMarne Context triple: [Vitry-sur-Seine, populationRankInValDeMarne, one of the most populous communes]
-
A.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
B.
hasPopulationRankInDepartment
chosen
Indicates the relative position of an entity’s population size compared to other entities within the same department.
-
C.
economicRankInFrance
Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
-
D.
locatedInMetropolitanFrance
Indicates that the subject is geographically situated within the territory of metropolitan (continental) France.
-
E.
sharesLaDéfenseDistrictWith
Indicates that two entities are located in, or associated with, the same La Défense district.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46ea8accc8190beea0900b0614020 |
completed | April 19, 2026, 5:56 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 9:55 a.m.