Triple
T27134948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ESCA |
E681655
|
entity |
| Predicate | analysisMode |
P164491
|
FINISHED |
| Object | survey scan |
—
|
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: survey scan | Statement: [ESCA, analysisMode, survey scan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: analysisMode Context triple: [ESCA, analysisMode, survey scan]
-
A.
analysisType
Indicates the specific kind or category of analysis being applied or performed in relation to an entity or dataset.
-
B.
analyzes
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
C.
helpsAnalyze
Indicates that one entity assists another in examining, interpreting, or understanding something in a more detailed or effective way.
-
D.
hasAnalysis
Indicates that an entity is associated with, or has undergone, a particular analysis or examination.
-
E.
unitOfAnalysis
Indicates the primary entity, level, or component that is being examined or measured in a given analysis or study.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f64e6e8c9081908ce4d364aa26147a |
completed | May 2, 2026, 7:20 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f64db8ee1881909362701d72ffe282 |
completed | May 2, 2026, 7:17 p.m. |
Created at: April 27, 2026, 9:06 a.m.