Triple
T4208581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air France Flight 447 |
E93840
|
entity |
| Predicate | crashArea |
P53562
|
FINISHED |
| Object | off the northeastern coast of Brazil |
—
|
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: off the northeastern coast of Brazil | Statement: [Air France Flight 447, crashArea, off the northeastern coast of Brazil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crashArea Context triple: [Air France Flight 447, crashArea, off the northeastern coast of Brazil]
-
A.
crashSiteRegion
chosen
Indicates the geographic region in which a crash site is located.
-
B.
crashCountry
Indicates that an aviation crash event occurred within the territory or jurisdiction of a specified country.
-
C.
reportingArea
Indicates that one entity serves as the geographic or organizational area for which information, data, or events about another entity are reported or aggregated.
-
D.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
E.
regionOfLoss
Indicates the anatomical or spatial area where a loss, damage, or deficit occurs or is localized.
- 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_69b3451743608190808f41d17ccf2650 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69b347efd9b08190bb50f82e4e7fe06d |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:03 p.m.