Triple
T28269477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic of China politics |
E712803
|
entity |
| Predicate | experiencedMartialLaw |
P51849
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Republic of China politics, experiencedMartialLaw, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: experiencedMartialLaw Context triple: [Republic of China politics, experiencedMartialLaw, yes]
-
A.
introducedMartialLaw
chosen
Indicates that an authority or governing body has imposed martial law over a population or territory.
-
B.
experiencedMilitaryDictatorship
Indicates that an entity has undergone a period of governance under a military-led authoritarian regime.
-
C.
wasMilitarized
Indicates that an entity underwent a process of being organized, equipped, or adapted for military use or purposes.
-
D.
isMilitarized
Indicates that an entity is organized, equipped, or structured according to military principles, forces, or purposes.
-
E.
wasMilitarizedDuring
Indicates that an entity transitioned to or adopted a military character, role, or control during a specified time period or event.
- 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_69efb5216c6881908020dce4aea65381 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 27, 2026, 11:17 p.m.