Triple
T34716346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Pavois |
E1000780
|
entity |
| Predicate | approximateExhibitorsPerYear |
P143910
|
FINISHED |
| Object | over 700 |
—
|
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 700 | Statement: [Grand Pavois, approximateExhibitorsPerYear, over 700]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateExhibitorsPerYear Context triple: [Grand Pavois, approximateExhibitorsPerYear, over 700]
-
A.
exhibitorsNumber
chosen
Indicates the total count of exhibitors associated with a given event or exhibition.
-
B.
exhibitionFrequency
Indicates how often an entity is displayed, presented, or exhibited within a given context or time period.
-
C.
hasExhibitors
Indicates that an entity includes, hosts, or is associated with one or more exhibitors.
-
D.
numberOfExhibits
Indicates the total count of exhibits associated with a given entity or context.
-
E.
exhibitorsFrom
Indicates a relationship where certain exhibitors originate from, are associated with, or come from a specified source, location, or organization.
- 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_69f76dad3f108190a280fd0a2f4ee89a |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff2eb19ad88190915fbbe08e8bc84e |
completed | May 9, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69ff2db5dd608190b7b7ba95f19c276c |
completed | May 9, 2026, 12:51 p.m. |
Created at: May 3, 2026, 3:59 p.m.