Triple
T4080457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Very Large Array |
E87465
|
entity |
| Predicate | minimumBaseline |
P49994
|
FINISHED |
| Object | about 1 kilometer |
—
|
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: about 1 kilometer | Statement: [Very Large Array, minimumBaseline, about 1 kilometer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumBaseline Context triple: [Very Large Array, minimumBaseline, about 1 kilometer]
-
A.
minimumWidth
Indicates that there is a specified smallest allowable or required width for something in the relationship.
-
B.
baselineType
Indicates the type or category of a baseline used as a reference point for comparison or evaluation.
-
C.
isMinimumWhen
Indicates that a value or state is at its smallest or least level precisely under certain specified conditions or circumstances.
-
D.
hasBaselineLength
chosen
Indicates that one entity has a specified baseline length measurement in relation to another entity or reference.
-
E.
minimumNumber
Indicates that the associated value is the smallest or least quantity allowed, required, or observed within a given set or context.
- 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_69aed9435cf48190ad1da737c962d19d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc5204d881909829de15015aa50d |
completed | March 9, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69aef9082c2081908474f082a49bebc8 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:39 p.m.