Triple
T23349037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TS 41-series |
E591954
|
entity |
| Predicate | appliesTo |
P1129
|
FINISHED |
| Object | GSM networks |
—
|
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: GSM networks | Statement: [TS 41-series, appliesTo, GSM networks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GSM networks Context triple: [TS 41-series, appliesTo, GSM networks]
-
A.
GSM
chosen
GSM is a second-generation (2G) digital mobile communication standard that became the global foundation for cellular voice and basic data services.
-
B.
GSM
GSM is the common abbreviation for Great St Mary’s Church, the historic University Church located in the center of Cambridge, England.
-
C.
GSM
GSM is the three-letter IATA airport code assigned to Qeshm International Airport in Iran.
-
D.
GSM
GSM is a classic Onitsuka Tiger sneaker model inspired by vintage tennis shoes, known for its minimalist design and retro athletic style.
-
E.
Mobile Networks
Mobile Networks is a Nokia business segment focused on providing mobile telecommunications infrastructure, technologies, and services for wireless network operators.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f199cb2a3c8190a5c0c8d8735256c7 |
completed | April 29, 2026, 5:40 a.m. |
Created at: April 17, 2026, 5:19 p.m.