Triple
T9697337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siachen conflict |
E234686
|
entity |
| Predicate | primaryCasualtiesFrom |
P90744
|
FINISHED |
| Object | environmental conditions |
—
|
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: environmental conditions | Statement: [Siachen conflict, primaryCasualtiesFrom, environmental conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryCasualtiesFrom Context triple: [Siachen conflict, primaryCasualtiesFrom, environmental conditions]
-
A.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
B.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
C.
casualtiesType
Indicates the specific category or nature of casualties (e.g., killed, injured, missing) associated with an event or incident.
-
D.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
-
E.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d3c02e0819098d05c68805689f1 |
completed | April 1, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69cd03b641408190942464eaf174c6b5 |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd081a9c5c819093439be7e802ff85 |
completed | April 1, 2026, 11:57 a.m. |
Created at: March 30, 2026, 8:18 p.m.