Triple
T15764929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F-16 program |
E382194
|
entity |
| Predicate | exportCustomersCountEstimate |
P120240
|
FINISHED |
| Object | more than 25 countries |
—
|
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: more than 25 countries | Statement: [F-16 program, exportCustomersCountEstimate, more than 25 countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exportCustomersCountEstimate Context triple: [F-16 program, exportCustomersCountEstimate, more than 25 countries]
-
A.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
B.
hasCustomers
Indicates that an entity maintains a business relationship in which other entities purchase or receive its goods or services as customers.
-
C.
initialCustomers
Indicates that the referenced entities are customers present or involved at the starting point of a process, period, or system.
-
D.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
E.
userCount
Indicates the number of users associated with or involved in a given context or entity.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b8154881908afe5191e6424f15 |
completed | April 16, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e03cc871d0819085c0fc54de7984ff |
completed | April 16, 2026, 1:35 a.m. |
Created at: April 10, 2026, 4:47 a.m.