Triple
T3824364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Pavilion |
E88649
|
entity |
| Predicate | visitorCountDuringExpo |
P427
|
FINISHED |
| Object | over 5 million |
—
|
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 5 million | Statement: [United States Pavilion, visitorCountDuringExpo, over 5 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitorCountDuringExpo Context triple: [United States Pavilion, visitorCountDuringExpo, over 5 million]
-
A.
visitorCount
chosen
Indicates the number of visitors associated with a particular entity, context, or time period.
-
B.
numberOfExhibits
Indicates the total count of exhibits associated with a given entity or context.
-
C.
visitorFrequency
Indicates how often a visitor comes to or interacts with a particular entity or location.
-
D.
hasExhibitors
Indicates that an entity includes, hosts, or is associated with one or more exhibitors.
-
E.
impactOnExhibitors
Indicates the effect or consequences that an event, decision, or condition has on exhibitors.
- 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef188b474819087680db42b04ecdd |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee74a2bc081909b237df8b1e27653 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:17 p.m.