Triple
T20076618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arctic (2018 film) |
E499882
|
entity |
| Predicate | featuresAccidentType |
P1788
|
FINISHED |
| Object | plane crash |
—
|
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: plane crash | Statement: [Arctic (2018 film), featuresAccidentType, plane crash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresAccidentType Context triple: [Arctic (2018 film), featuresAccidentType, plane crash]
-
A.
accidentType
chosen
Indicates the specific category or kind of accident associated with an event or incident.
-
B.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
C.
accidentSeverity
Indicates the level or degree of seriousness associated with an accident.
-
D.
secondAccidentType
Indicates the type or category of a second (subsequent) accident associated with an entity or event.
-
E.
accidentSequenceFeature
Indicates that one event or condition functions as a feature or component within the ordered sequence of circumstances leading to an accident.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6643bcfd48190893abd3734f15cd3 |
completed | April 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69e54cf369b88190931532420517dac7 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:40 p.m.