Triple
T7985524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MapReduce |
E185673
|
entity |
| Predicate | scalesTo |
P74395
|
FINISHED |
| Object | thousands of machines |
—
|
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: thousands of machines | Statement: [MapReduce, scalesTo, thousands of machines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scalesTo Context triple: [MapReduce, scalesTo, thousands of machines]
-
A.
scalesWith
chosen
Indicates that a change in one quantity is systematically associated with a proportional or otherwise dependent change in another quantity.
-
B.
coversScale
Indicates that one entity spans, includes, or applies across the full range or extent of another entity’s scale.
-
C.
hasScales
Indicates that an entity possesses scales as a surface covering or body feature.
-
D.
hasScale
Indicates that one entity possesses or is characterized by a scale or graduated measurement system related to another entity.
-
E.
similarScaleTo
Indicates that two entities have comparable magnitude, size, or extent along a given dimension or measurement scale.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4a55b881909a96133e56c0dffa |
completed | March 31, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69cb048009a08190b4c577208a9f8f76 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:15 p.m.