Triple
T17694154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Universal Value Function Approximators |
E441116
|
entity |
| Predicate | goalRepresentation |
P72446
|
FINISHED |
| Object | can be discrete |
—
|
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: can be discrete | Statement: [Universal Value Function Approximators, goalRepresentation, can be discrete]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: goalRepresentation Context triple: [Universal Value Function Approximators, goalRepresentation, can be discrete]
-
A.
goalStructure
Indicates the overarching objective or intended outcome that organizes and guides the structure of related actions, components, or relationships.
-
B.
goalDescription
Indicates that an entity expresses, specifies, or provides a textual description of a goal or intended outcome associated with another entity or activity.
-
C.
goalType
chosen
Indicates the specific category or nature of a goal associated with an entity or action.
-
D.
goalAssociatedWith
Indicates that a goal is connected or linked to another entity, such as an activity, plan, or outcome, in a way that reflects relevance or influence.
-
E.
machineGoal
Indicates that a machine or automated system has a specific objective, target state, or outcome it is intended or programmed to achieve.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4715485d88190b9b6f347ff85d7c7 |
completed | April 19, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 10:04 a.m.