Triple
T33164453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keiji Kiriya |
E848840
|
entity |
| Predicate | trainingStatusAtStart |
P88969
|
FINISHED |
| Object | rookie soldier |
—
|
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: rookie soldier | Statement: [Keiji Kiriya, trainingStatusAtStart, rookie soldier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingStatusAtStart Context triple: [Keiji Kiriya, trainingStatusAtStart, rookie soldier]
-
A.
trainingStatus
Indicates the current state or phase of an entity within a training or learning process.
-
B.
trainingStartState
chosen
Indicates the initial condition or status of an entity at the moment a training process begins.
-
C.
engagementStatusAtStart
Indicates the engagement status or condition of the relationship at its initial or starting point.
-
D.
initialStatus
Indicates the original or starting state assigned to an entity before any changes or updates occur.
-
E.
initiationStatus
Indicates the current state or phase of beginning or starting a process, activity, or relationship between entities.
- 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_69f3495be8808190bbf427733df08aad |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f70fb4f18c819099ef6d9177b7d205 |
completed | May 3, 2026, 9:04 a.m. |
| PD | Predicate disambiguation | batch_69f70f3a54d481909ba6bdda3647b761 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:28 a.m.