Triple
T14987471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boulevard Périphérique |
E373740
|
entity |
| Predicate | averageDailyTraffic |
P99819
|
FINISHED |
| Object | over one million vehicles |
—
|
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: over one million vehicles | Statement: [Boulevard Périphérique, averageDailyTraffic, over one million vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageDailyTraffic Context triple: [Boulevard Périphérique, averageDailyTraffic, over one million vehicles]
-
A.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
B.
hasDailyTraffic
chosen
Indicates that an entity experiences or is associated with a certain amount or pattern of traffic on a daily basis.
-
C.
trafficShare
Indicates the proportion of total traffic or visits that one entity receives relative to others within a defined context or time period.
-
D.
touristTraffic
Indicates the level, flow, or intensity of tourists visiting or moving through a particular place or area.
-
E.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7007588819095bb1de029a6f2eb |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:53 a.m.