Triple
T37105369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 6489 Golevka |
E918824
|
entity |
| Predicate | hasRadarModel |
P195664
|
FINISHED |
| Object | three-dimensional shape model derived from radar data |
—
|
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: three-dimensional shape model derived from radar data | Statement: [6489 Golevka, hasRadarModel, three-dimensional shape model derived from radar data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRadarModel Context triple: [6489 Golevka, hasRadarModel, three-dimensional shape model derived from radar data]
-
A.
hasRadarControlRole
Indicates that an entity holds a role or responsibility related to managing or controlling radar operations.
-
B.
hasRadarServices
Indicates that one entity provides or is equipped with radar-based services or capabilities for another entity or context.
-
C.
radarEquipped
Indicates that an entity is equipped with or has access to radar detection equipment or radar-based sensing capability.
-
D.
radarModel
Indicates that one entity is a radar system and the other is the specific model or type designation of that radar.
-
E.
radarType
Indicates the specific category or classification of radar associated with an entity.
- F. None of above. chosen
Provenance (4 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_69f76e9b99c8819096164b21ff5bd996 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fddd373cdc8190be1b12e70e4deb1f |
completed | May 8, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69fddc6915a88190ad41e379aa3ede13 |
completed | May 8, 2026, 12:51 p.m. |
| PDg | Predicate description generation | batch_69fddd364c1481908794c9d423bdc2d7 |
completed | May 8, 2026, 12:55 p.m. |
Created at: May 3, 2026, 4:14 p.m.