Triple
T385687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IEEE MTT-S International Microwave Symposium |
E8775
|
entity |
| Predicate | rotatingLocation |
P10763
|
FINISHED |
| Object | various cities in the United States |
—
|
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: various cities in the United States | Statement: [IEEE MTT-S International Microwave Symposium, rotatingLocation, various cities in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rotatingLocation Context triple: [IEEE MTT-S International Microwave Symposium, rotatingLocation, various cities in the United States]
-
A.
coordinateLocation
Indicates that an entity is located at, or associated with, a specific geographic coordinate or set of coordinates.
-
B.
rotationType
Indicates the specific kind or mode of rotational movement or orientation applied in a given context.
-
C.
locatedAlong
Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
-
D.
locationOnUniform
Indicates that a specific location or marking appears on a uniform.
-
E.
transformationLocation
Indicates the place or spatial context where a transformation or change of state occurs.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec4345a48190a413261cba4eafd7 |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e967d84c8190a6b647f78d95d4e4 |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2ea2dc3088190a2aeb4496aff3582 |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.