Triple
T18618199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mavericks |
E455082
|
entity |
| Predicate | hasFatalAccident |
P32122
|
FINISHED |
| Object | Mark Foo in 1994 |
—
|
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: Mark Foo in 1994 | Statement: [Mavericks, hasFatalAccident, Mark Foo in 1994]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFatalAccident Context triple: [Mavericks, hasFatalAccident, Mark Foo in 1994]
-
A.
fatalAccident
Indicates that an accident resulted in at least one death.
-
B.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
C.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
D.
hasModeOfTransportInAccident
Indicates that a specific mode of transport was involved in an accident associated with the given entity.
-
E.
involvedInAccident
chosen
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
- 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_69d8d38bbe7c8190bdec3138e7d413c9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54d06c1f8819094fbfa56eca07eaa |
completed | April 19, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69e478cf5e888190a0b1074b0c6525df |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:46 a.m.