Triple
T21192370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMS |
E522242
|
entity |
| Predicate | extends |
P1244
|
FINISHED |
| Object | SMS |
—
|
NE NERFINISHED |
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: SMS | Statement: [MMS, extends, SMS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SMS Context triple: [MMS, extends, SMS]
-
A.
SMS
chosen
SMS (Short Message Service) is a standardized text messaging service that allows mobile devices to exchange short alphanumeric messages over cellular networks.
-
B.
SMS
SMS is a third-generation 8-bit home video game console developed and released by Sega as a competitor to Nintendo’s NES.
-
C.
SMS Saida
SMS Saida was a light cruiser of the Austro-Hungarian Navy that served during World War I in the Adriatic Sea.
-
D.
IMS
IMS (IP Multimedia Subsystem) is a standardized architectural framework for delivering IP-based multimedia services over mobile and fixed networks.
-
E.
IMS
IMS is a leading biomedical research institute focused on understanding metabolic diseases such as obesity and diabetes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73338dbe881908360dedad65c964b |
completed | April 21, 2026, 8:20 a.m. |
Created at: April 16, 2026, 3:07 p.m.