Triple
T18459282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludendorff Bridge |
E450987
|
entity |
| Predicate | casualtiesAtCollapse |
P10775
|
FINISHED |
| Object | U.S. Army engineers killed and injured |
—
|
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: U.S. Army engineers killed and injured | Statement: [Ludendorff Bridge, casualtiesAtCollapse, U.S. Army engineers killed and injured]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesAtCollapse Context triple: [Ludendorff Bridge, casualtiesAtCollapse, U.S. Army engineers killed and injured]
-
A.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
B.
primaryCasualtiesFrom
Indicates that an entity is the main source or cause of the casualties experienced by another entity.
-
C.
casualtiesDescription
chosen
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
-
D.
demolitionCasualties
Indicates that an act of demolition resulted in injuries or deaths to people involved or nearby.
-
E.
secondaryCasualtiesFrom
Indicates that an entity experiences indirect or collateral harm as a consequence of another primary event or source.
- 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_69d8d38345688190b565eac2e4cd7935 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e52a7cdb5c8190a399f0e4052f7d1f |
completed | April 19, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e469d05cf4819099baf1665a9cf18a |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:33 a.m.