Triple
T20814174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lockheed Martin X-33 |
E512390
|
entity |
| Predicate | numberOfFlightsPlanned |
P4899
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [Lockheed Martin X-33, numberOfFlightsPlanned, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFlightsPlanned Context triple: [Lockheed Martin X-33, numberOfFlightsPlanned, 15]
-
A.
numberOfFlights
chosen
Indicates the total count of flights associated with a given entity or within a specified context.
-
B.
hasScheduledFlights
Indicates that there are one or more flights planned and set to occur between the related entities according to a schedule.
-
C.
numberOfFreeFlights
Indicates the quantity of flights available at no cost to a given entity within a specified context.
-
D.
numberOfCaptiveFlights
Indicates the number of flights during which an entity is held or transported in captivity.
-
E.
quantityFlown
Indicates the amount or volume that has been transported by flying from one place to another.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d4e43c8190aecce82a3f7e2de0 |
completed | April 21, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69e5c99ca55481908e8d434fa901cfd6 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:40 p.m.