Triple
T7272481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Very Long Baseline Array |
E161138
|
entity |
| Predicate | hasDishDiameter |
P21268
|
FINISHED |
| Object | 25 meters |
—
|
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: 25 meters | Statement: [Very Long Baseline Array, hasDishDiameter, 25 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDishDiameter Context triple: [Very Long Baseline Array, hasDishDiameter, 25 meters]
-
A.
dishDiameter
chosen
Indicates the measurement of how wide a dish is across its center.
-
B.
approximateDiameter
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
-
C.
dishShape
Indicates that one entity has the physical form or contour characterized by the other entity as its shape.
-
D.
fanDiameter
Indicates the size of a fan measured as the length of a straight line passing through its center from one edge to the opposite edge.
-
E.
driverDiameter
Indicates the size of the circular cross-section of a driver component, typically measured as the distance across its widest point.
- 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_69c6885181008190b419040e22939c7c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb8a0b4881908ff27c5a75bd4a95 |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76a84a081908d4184c55b728e48 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:58 p.m.