Triple
T4699124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gap |
E104222
|
entity |
| Predicate | populationRankInDepartment |
P45844
|
FINISHED |
| Object | largest city in Hautes-Alpes |
—
|
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: largest city in Hautes-Alpes | Statement: [Gap, populationRankInDepartment, largest city in Hautes-Alpes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationRankInDepartment Context triple: [Gap, populationRankInDepartment, largest city in Hautes-Alpes]
-
A.
hasPopulationRankInDepartment
chosen
Indicates the relative position of an entity’s population size compared to other entities within the same department.
-
B.
capitalOfDepartment
Indicates that a city or town serves as the administrative capital of a specified department (an administrative division).
-
C.
prefectureOfDepartment
Indicates that a given prefecture administers or is the capital authority of a specified department.
-
D.
populationRankInCounty
Indicates the relative position of an entity in terms of population size compared to other entities within the same county.
-
E.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
- 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_69bd43e9b88481908582103dcadff3d9 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd650ad0f88190844bfcb46b3071c2 |
completed | March 20, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69bd621ba7448190a53ab1e2897acf71 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:17 p.m.