Triple
T4961409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyon metropolitan area |
E111415
|
entity |
| Predicate | economicRankInFrance |
P61452
|
FINISHED |
| Object | one of the largest economic centers in 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: one of the largest economic centers in France | Statement: [Lyon metropolitan area, economicRankInFrance, one of the largest economic centers in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicRankInFrance Context triple: [Lyon metropolitan area, economicRankInFrance, one of the largest economic centers in France]
-
A.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
B.
locatedInMetropolitanFrance
Indicates that the subject is geographically situated within the territory of metropolitan (continental) France.
-
C.
statusInFrance
Indicates the legal, social, or official standing or condition that an entity has within the jurisdiction of France.
-
D.
strengthFrance
Indicates a relationship where a level, measure, or attribute of strength is associated specifically with France.
-
E.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
- 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_69bd4419393c819086319a6fe4bf8542 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd72e49b048190bac55d9e7a6f7963 |
completed | March 20, 2026, 4:16 p.m. |
| PD | Predicate disambiguation | batch_69bd71447fe88190bb62c5e8753da7a7 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd72e1b7cc8190b2e621fdf8f22e38 |
completed | March 20, 2026, 4:16 p.m. |
Created at: March 20, 2026, 1:32 p.m.