Triple
T2751978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens ACS-64 |
E61008
|
entity |
| Predicate | orderQuantity |
P41646
|
FINISHED |
| Object | 70 units for Amtrak |
—
|
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 for Amtrak | Statement: [Siemens ACS-64, orderQuantity, 70 units for Amtrak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderQuantity Context triple: [Siemens ACS-64, orderQuantity, 70 units for Amtrak]
-
A.
orderType
Indicates the specific category or classification of an order, such as its purpose, channel, or processing method.
-
B.
numberOfOrders
Indicates the total count of orders associated with a given entity or context.
-
C.
usesQuantity
Indicates that one entity employs or applies a specified amount or measure of another entity in performing an action or fulfilling a function.
-
D.
order
Indicates that one entity requests, arranges, or directs that another entity provide a good, service, or action, typically in a specified sequence or priority.
-
E.
orderOf
Indicates that one entity is arranged, ranked, or sequenced before or after another according to a specified ordering criterion.
- F. None of above. chosen
Provenance (4 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb6d08088190b489de15a120ba3f |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd82d005c81908a1ac7a1313c6d88 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd91497ec8190927e91ad33549eda |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:56 p.m.