Triple
T36128600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 中国人民解放军预备役人员 |
E1044952
|
entity |
| Predicate | 服役形态 |
P10078
|
FINISHED |
| Object | 非现役 |
—
|
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: 非现役 | Statement: [中国人民解放军预备役人员, 服役形态, 非现役]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 服役形态 Context triple: [中国人民解放军预备役人员, 服役形态, 非现役]
-
A.
militaryFunction
Indicates a relationship where an entity serves a specific role, duty, or operational purpose within a military context.
-
B.
militaryCharacteristic
Indicates that one entity possesses a specific military-related attribute, quality, or feature in relation to another entity or context.
-
C.
militaryStatus
chosen
Indicates the relationship between an entity and a military organization in terms of service condition, such as active duty, reserve, veteran, or non-military status.
-
D.
usedMilitaryFormation
Indicates that an entity employed a specific military formation or tactical arrangement of forces in a military context.
-
E.
hasMilitaryType
Indicates that an entity is associated with or classified under a specific military category, role, or type.
- 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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b3e2f3c08190be4fd1ae4fa1266d |
completed | May 3, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bcc47081909fe7d592ac69006c |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:08 p.m.