Triple
T36489579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ENAS |
E899017
|
entity |
| Predicate | searchGranularity |
P109501
|
FINISHED |
| Object | cell-level architecture search |
—
|
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: cell-level architecture search | Statement: [ENAS, searchGranularity, cell-level architecture search]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: searchGranularity Context triple: [ENAS, searchGranularity, cell-level architecture search]
-
A.
granularityLevel
chosen
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
B.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
C.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
D.
outputGranularity
Indicates the level of detail or resolution at which results or data are produced or reported.
-
E.
timeTravelGranularity
Indicates the level of temporal precision or resolution at which time travel or time-based operations can occur between entities.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf769338819092a5f42653dcc956 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:10 p.m.