Triple
T9992129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GEMA |
E196909
|
entity |
| Predicate | technologyApplication |
P41521
|
FINISHED |
| Object | shipborne radar |
—
|
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: shipborne radar | Statement: [GEMA, technologyApplication, shipborne radar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: technologyApplication Context triple: [GEMA, technologyApplication, shipborne radar]
-
A.
technologyUse
Indicates the use or application of a particular technology by an entity to perform an action or achieve a purpose.
-
B.
technologyUsedIn
chosen
Indicates that a particular technology is employed or applied within a specific process, product, context, or domain.
-
C.
technologyType
Indicates the specific kind or category of technology associated with an entity or relationship.
-
D.
technologyContext
Indicates the technological setting, tools, or systems within which an action, relationship, or process occurs.
-
E.
technologyAdvantage
Indicates that one entity possesses a superior or more advanced technological capability compared to another entity.
- 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_69ca82f1678c819093d06320a05f16a4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcb95842c8190b8cdce9584f19840 |
completed | April 2, 2026, 1:51 a.m. |
| PD | Predicate disambiguation | batch_69cd1da07db88190945bcdab3ca82e71 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:50 p.m.