Triple
T33518954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LMA |
E858447
|
entity |
| Predicate | effortFactor |
P93837
|
FINISHED |
| Object | Space (direct–indirect) |
—
|
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: Space (direct–indirect) | Statement: [LMA, effortFactor, Space (direct–indirect)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effortFactor Context triple: [LMA, effortFactor, Space (direct–indirect)]
-
A.
dimensionOfEffort
chosen
Indicates a specific measurable aspect or component that characterizes the nature or extent of an effort.
-
B.
focusFactor
Indicates that one entity serves as a measure, component, or determinant of the degree of focus or concentration associated with another entity.
-
C.
hasPlanningEffort
Indicates that an entity is associated with a required or invested amount of planning work or preparation effort.
-
D.
skillFactor
Indicates the degree or level of skill associated with an entity in performing a particular task or activity.
-
E.
efficiency
Indicates how effectively an entity converts inputs (such as time, energy, or resources) into desired outputs or results.
- 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_69f349781c6c819082c516b260efe7e2 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:39 a.m.