Triple
T2198027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Boy |
E50421
|
entity |
| Predicate | casualtiesContext |
P10775
|
FINISHED |
| Object | caused massive civilian casualties in Hiroshima |
—
|
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: caused massive civilian casualties in Hiroshima | Statement: [Little Boy, casualtiesContext, caused massive civilian casualties in Hiroshima]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesContext Context triple: [Little Boy, casualtiesContext, caused massive civilian casualties in Hiroshima]
-
A.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
B.
casualtiesDescription
chosen
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
C.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
-
D.
casualtiesIncluded
Indicates that the referenced count or report of casualties explicitly includes the specified individuals or groups.
-
E.
casualtiesType
Indicates the specific category or nature of casualties (e.g., killed, injured, missing) associated with an event or incident.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf79f3e08190b56e9d7c0ff27237 |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbda706f4819094de73e1d1d1f539 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.