Triple
T9843073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle Efficiency Award |
E239272
|
entity |
| Predicate | typeOfReadiness |
P11883
|
FINISHED |
| Object | administrative readiness |
—
|
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: administrative readiness | Statement: [Battle Efficiency Award, typeOfReadiness, administrative readiness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfReadiness Context triple: [Battle Efficiency Award, typeOfReadiness, administrative readiness]
-
A.
readinessCategory
chosen
Indicates the classification of an entity based on its current level or state of preparedness for a given purpose or activity.
-
B.
operationalReadiness
Indicates that an entity is in a state where it is fully prepared, equipped, and able to perform its intended function or mission.
-
C.
hasReadingType
Indicates that an entity is associated with a specific category or mode of reading, such as a particular interpretation, format, or type of reading measurement.
-
D.
typeOfState
Indicates that one state is a specific kind or category of another, more general state.
-
E.
isProductionReady
Indicates that something has met all necessary quality, stability, and performance criteria to be safely deployed and used in a live production environment.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb35c8e348190aa090c71bf6f30eb |
completed | April 2, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69cd03e57cac8190914bb5ae608a6e0e |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:33 p.m.