Triple
T21803313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Papantla Flyers |
E538288
|
entity |
| Predicate | numberOfFlyersDescending |
P145704
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Papantla Flyers, numberOfFlyersDescending, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFlyersDescending Context triple: [Papantla Flyers, numberOfFlyersDescending, 4]
-
A.
verticalDrop_ft
Indicates the vertical distance, measured in feet, that one entity drops or falls relative to another reference level.
-
B.
dropsFrom
Indicates that one entity falls, descends, or is released from another entity as its source or origin.
-
C.
verticalDrop_m
Indicates the vertical distance, measured in meters, through which something drops or falls from a higher point to a lower point.
-
D.
numberOfFlights
Indicates the total count of flights associated with a given entity or within a specified context.
-
E.
quantityFlown
Indicates the amount or volume that has been transported by flying from one place to another.
- F. None of above. chosen
Provenance (4 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_69e0c4733f4081909a86622e7e6d15d2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0780126e88190a93dd8d0519eb8fc |
completed | April 28, 2026, 9:04 a.m. |
| PD | Predicate disambiguation | batch_69e6be751ce881909badced245ef76c7 |
completed | April 21, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69e6c3a2898881909748935cf92f898c |
completed | April 21, 2026, 12:24 a.m. |
Created at: April 16, 2026, 6:53 p.m.