Triple
T12949943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramstein airshow disaster |
E309865
|
entity |
| Predicate | numberOfSeriouslyInjured |
P63693
|
FINISHED |
| Object | approximately 346 |
—
|
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: approximately 346 | Statement: [Ramstein airshow disaster, numberOfSeriouslyInjured, approximately 346]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeriouslyInjured Context triple: [Ramstein airshow disaster, numberOfSeriouslyInjured, approximately 346]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
-
C.
accidentSeverity
Indicates the level or degree of seriousness associated with an accident.
-
D.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
E.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:43 p.m.