Triple
T12830089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flight over Vienna |
E306759
|
entity |
| Predicate | numberOfLeafletsDropped |
P66970
|
FINISHED |
| Object | approximately 400,000 |
—
|
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: approximately 400,000 | Statement: [Flight over Vienna, numberOfLeafletsDropped, approximately 400,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLeafletsDropped Context triple: [Flight over Vienna, numberOfLeafletsDropped, approximately 400,000]
-
A.
numberOfLeaflets
chosen
Indicates the count of individual leaflets associated with or contained within a given entity.
-
B.
leafletsPerLeaf
Indicates the number of individual leaflets that are present on each single leaf.
-
C.
numberOfLeaves
Indicates the specific count of leaves associated with an entity.
-
D.
currentNumberOfLeaves
Indicates the present count of leaves associated with an entity at a given point in time.
-
E.
originalNumberOfLeaves
Indicates the initial count of leaves associated with an entity before any changes, losses, or additions occur.
- 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714208f881908f7f8a921362909a |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa08cd481909a946046ba63809f |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:34 p.m.