Triple
T20005332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lumen Industries |
E494442
|
entity |
| Predicate | procedureEffect |
P106972
|
FINISHED |
| Object | splits employees’ memories into work and personal selves |
—
|
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: splits employees’ memories into work and personal selves | Statement: [Lumen Industries, procedureEffect, splits employees’ memories into work and personal selves]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: procedureEffect Context triple: [Lumen Industries, procedureEffect, splits employees’ memories into work and personal selves]
-
A.
tookEffect
Indicates that a change, rule, condition, or event became active, operative, or started producing its intended consequences.
-
B.
sideEffect
Indicates that one entity is an unintended or secondary effect resulting from the use or occurrence of another entity.
-
C.
measuredEffect
Indicates that an action or process has produced a specific, quantified outcome or impact on something.
-
D.
programOperation
Indicates an operation or action performed by, within, or upon a program in a computational or procedural context.
-
E.
providesEffect
chosen
Indicates that one entity causes, delivers, or produces a particular effect or outcome on another entity.
- 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a46c748190a141ab5aac6ea250 |
completed | April 20, 2026, 5:25 p.m. |
| PD | Predicate disambiguation | batch_69e54cdddbd48190becc8b2aa5ab4ef9 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:33 p.m.