Triple
T9580721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 Fort Totten crash |
E231160
|
entity |
| Predicate | railwayAccidentClass |
P1788
|
FINISHED |
| Object | signal system failure-related accident |
—
|
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: signal system failure-related accident | Statement: [2009 Fort Totten crash, railwayAccidentClass, signal system failure-related accident]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayAccidentClass Context triple: [2009 Fort Totten crash, railwayAccidentClass, signal system failure-related accident]
-
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.
accident
Indicates an unintended, unforeseen event or mishap occurring, often resulting in damage, injury, or disruption.
-
D.
railroadClass
Indicates the classification or category of a railroad according to an established system (e.g., by size, revenue, or regulatory status).
-
E.
siteOfAccident
Indicates the location where an accident occurred.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cbe79081909947b4d1389eb015 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.