Triple
T9580699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 Fort Totten crash |
E231160
|
entity |
| Predicate | leadingTrainStatus |
P41715
|
FINISHED |
| Object | stopped on track |
—
|
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: stopped on track | Statement: [2009 Fort Totten crash, leadingTrainStatus, stopped on track]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadingTrainStatus Context triple: [2009 Fort Totten crash, leadingTrainStatus, stopped on track]
-
A.
railStatus
chosen
Indicates the current operational condition or state of a rail-related entity (such as a track, line, or service) within the system.
-
B.
railwayTraffic
Indicates the presence, flow, or management of train movements along railway lines between locations.
-
C.
railLineStatusAfterward
Indicates the condition or operational status of a rail line after a specified event or time period has occurred.
-
D.
trainsOn
Indicates that one entity receives training, instruction, or practice using or based on another entity (such as a resource, dataset, tool, or subject).
-
E.
trainNumberDirection
Indicates the specific direction in which a train, identified by its train number, is traveling or scheduled to travel.
- 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.