Triple
T4980216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IEC 62591 |
E111864
|
entity |
| Predicate | defines |
P264
|
FINISHED |
| Object | WirelessHART |
E20658
|
NE 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: WirelessHART | Statement: [IEC 62591, defines, WirelessHART]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WirelessHART Context triple: [IEC 62591, defines, WirelessHART]
-
A.
WirelessHART
chosen
WirelessHART is an industrial wireless communication standard designed for reliable, secure, and interoperable field device networking in process automation environments.
-
B.
HART
HART is the public bus transportation system serving the Huntington area, providing local transit services to residents and visitors.
-
C.
HART Communication Foundation
The HART Communication Foundation is an industry consortium that develops and maintains the HART and WirelessHART communication standards for smart field instruments and process automation.
-
D.
Zigbee
Zigbee is a low-power, wireless mesh networking standard commonly used for home automation and Internet of Things (IoT) devices.
-
E.
MindSphere
MindSphere is Siemens' cloud-based industrial Internet of Things (IIoT) platform for connecting, monitoring, and analyzing data from industrial equipment and systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7251b7648190bbb0acf0b9148ae6 |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a0f90048190998dad99555891c0 |
completed | March 21, 2026, 12:07 p.m. |
Created at: March 20, 2026, 1:33 p.m.