Triple
T9126345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saw Maung |
E218975
|
entity |
| Predicate | responseToProtests |
P87232
|
FINISHED |
| Object | military crackdown |
—
|
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: military crackdown | Statement: [Saw Maung, responseToProtests, military crackdown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: responseToProtests Context triple: [Saw Maung, responseToProtests, military crackdown]
-
A.
protests
Indicates that an entity publicly expresses opposition or disapproval toward another entity, action, or situation.
-
B.
hasProtestMovement
Indicates that an entity is associated with, or gives rise to, an organized protest movement opposing or advocating change related to it.
-
C.
teamDuringProtest
Indicates that two or more entities are part of the same team or organized group specifically in the context of a protest event.
-
D.
notableProtest
Indicates that an entity is recognized for having led, organized, or been centrally involved in a significant protest or demonstration.
-
E.
hasLanguageOfProtest
Indicates that an entity expresses or embodies a language, style, or discourse of protest in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8c93d3c8190b003b2b1af2003b2 |
completed | April 1, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69cc66003e3c819091e1e42c9cf7c781 |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6a3c78388190a7436acc0e44ff55 |
completed | April 1, 2026, 12:43 a.m. |
Created at: March 30, 2026, 7:18 p.m.