Triple
T2426753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald J. Kutyna |
E53545
|
entity |
| Predicate | serviceNumberOfMissions |
P7449
|
FINISHED |
| Object | multiple space-related investigative and advisory roles |
—
|
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: multiple space-related investigative and advisory roles | Statement: [Donald J. Kutyna, serviceNumberOfMissions, multiple space-related investigative and advisory roles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceNumberOfMissions Context triple: [Donald J. Kutyna, serviceNumberOfMissions, multiple space-related investigative and advisory roles]
-
A.
numberOfMissions
chosen
Indicates the total count of missions associated with a given entity or context.
-
B.
numberOfMemberStateMissions
Indicates the total count of missions associated with or undertaken by the member states in a given context.
-
C.
missionNumber
Indicates the identifying number assigned to a specific mission within a sequence or set of missions.
-
D.
typeOfMission
Indicates the specific category or nature of a mission that an entity is associated with or engaged in.
-
E.
numberOfUncrewedMissions
Indicates the total count of missions carried out without any crew on board.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcc74a5108190a3a9631b0cc1a127 |
completed | March 7, 2026, 6:57 a.m. |
| PD | Predicate disambiguation | batch_69abc5aa1b60819081b87f7985c6cff3 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:42 p.m.