Triple
T214588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Positive Train Control |
E4790
|
entity |
| Predicate | canInterveneBy |
P7702
|
FINISHED |
| Object | automatically applying brakes |
—
|
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: automatically applying brakes | Statement: [Positive Train Control, canInterveneBy, automatically applying brakes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canInterveneBy Context triple: [Positive Train Control, canInterveneBy, automatically applying brakes]
-
A.
canEnforce
Indicates that one entity has the authority or capability to compel compliance with rules, decisions, or obligations upon another entity.
-
B.
canMake
Indicates that one entity has the ability or capacity to create, produce, or assemble another entity.
-
C.
canBe
Indicates that one entity has the potential, permission, or capability to become, perform as, or be classified as another entity.
-
D.
canRefer
Indicates that one entity has the ability or permission to mention, point to, or direct attention to another entity.
-
E.
usesIntervention
chosen
Indicates that one entity applies, employs, or relies on a specific intervention (such as a treatment, method, or strategy) in relation to another entity or context.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c32ae208190a03d504ef43ea659 |
completed | Feb. 28, 2026, 3:08 a.m. |
| PD | Predicate disambiguation | batch_69a25b509400819093a6c1a1bac861e3 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.