Triple
T7810071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing Model 299 |
E180654
|
entity |
| Predicate | fatalitiesInCrash |
P63692
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Boeing Model 299, fatalitiesInCrash, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fatalitiesInCrash Context triple: [Boeing Model 299, fatalitiesInCrash, 2]
-
A.
fatalitiesOnboard
Indicates that the relationship specifies the number of people who died among those present on a particular vehicle or craft.
-
B.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
C.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
D.
numberOfVictimsKilled
chosen
Indicates the count of victims who were killed as a result of the referenced event or action.
-
E.
casualtiesTotal
Indicates the total number of people killed and injured as a result of a particular 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78bb4b08190b2b3b51c5a0a033c |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae91687788190af9cb7aaa996d291 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:37 p.m.