Triple
T13498393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACS-64 |
E320818
|
entity |
| Predicate | orderQuantityForAmtrak |
P41646
|
FINISHED |
| Object | 70 units |
—
|
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: 70 units | Statement: [ACS-64, orderQuantityForAmtrak, 70 units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderQuantityForAmtrak Context triple: [ACS-64, orderQuantityForAmtrak, 70 units]
-
A.
orderQuantity
chosen
Indicates the specific amount or number of items requested or scheduled in an order transaction.
-
B.
vehiclesPerTrain
Indicates the number of vehicles that are attached to or make up a single train.
-
C.
hasExpressTracks
Indicates that a transportation route or facility includes tracks designated for express service, allowing faster travel with fewer stops than regular tracks.
-
D.
trainCount
Indicates the number of trains associated with a given entity, context, or time period.
-
E.
introducedByAmtrak
Indicates that something (such as a service, policy, feature, or change) was initiated, launched, or brought into use by Amtrak.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4fab688190bdc746985b0c7338 |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae06061881909a6a6032e0507587 |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:43 p.m.