Triple
T14681879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LZ 129 Hindenburg |
E344805
|
entity |
| Predicate | passengerFatalitiesInFinalAccident |
P33471
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [LZ 129 Hindenburg, passengerFatalitiesInFinalAccident, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerFatalitiesInFinalAccident Context triple: [LZ 129 Hindenburg, passengerFatalitiesInFinalAccident, 13]
-
A.
fatalitiesOnboard
chosen
Indicates that the relationship specifies the number of people who died among those present on a particular vehicle or craft.
-
B.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
C.
fatalAccident
Indicates that an accident resulted in at least one death.
-
D.
casualtiesHijackers
Indicates that the hijackers caused or were responsible for casualties (deaths or injuries) among others.
-
E.
numberOfVictimsConfirmed
Indicates the confirmed count of victims associated with an event, incident, or situation.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb56a51ec8190941684fd562a7182 |
completed | April 14, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69de6579fb7881909becc8f5822b39d4 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.