Triple
T2568007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Mobile |
E57595
|
entity |
| Predicate | primaryTechnology |
P38305
|
FINISHED |
| Object | GSM |
—
|
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: GSM | Statement: [Microsoft Mobile, primaryTechnology, GSM]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryTechnology Context triple: [Microsoft Mobile, primaryTechnology, GSM]
-
A.
technologyType
Indicates the specific kind or category of technology associated with an entity or relationship.
-
B.
primaryDeviceCategory
Indicates the main type or class of device associated with an entity or event, distinguishing it from other possible device categories.
-
C.
technologyBasis
chosen
Indicates that one entity is founded on, enabled by, or fundamentally relies upon the technology provided or represented by another entity.
-
D.
primarySystem
Indicates that one system is designated as the main or most important system in relation to another system or context.
-
E.
laterUsedTechnology
Indicates that one entity adopted or employed a technology after another entity had already used it.
- 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_69ab4a51410081908501dcf8bad9adc4 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd36191848190b6255fa9029429bd |
completed | March 7, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69abd0cc8d308190ae7aa32b8f5ae2e5 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:48 p.m.