Triple
T33589019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Argo |
E860366
|
entity |
| Predicate | aimsToSample |
P23062
|
FINISHED |
| Object | full ocean depth |
—
|
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: full ocean depth | Statement: [Deep Argo, aimsToSample, full ocean depth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aimsToSample Context triple: [Deep Argo, aimsToSample, full ocean depth]
-
A.
aimsToModel
Indicates that one entity is intended or designed to represent, simulate, or approximate the behavior, structure, or properties of another entity.
-
B.
aimsToMeasure
chosen
Indicates that one entity is intended or designed to quantify, assess, or evaluate another entity or property.
-
C.
aimOf
Indicates that one entity serves as the goal, purpose, or intended target of another entity’s action, plan, or existence.
-
D.
aimsToCapture
Indicates an intention or effort by one entity to take control of, seize, or gain possession of another entity.
-
E.
usesSamplingOf
Indicates that one entity employs or relies on a sample or subset derived from another entity for its operation, analysis, or processing.
- 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_69f3497e70e48190951c94d072879bec |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f7776ed08190833e15ea59b71b8d |
completed | May 3, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:40 a.m.