Triple
T18704678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Distinguished Engineer at Google |
E457339
|
entity |
| Predicate | impactScope |
P132368
|
FINISHED |
| Object | multiple products or infrastructure areas |
—
|
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: multiple products or infrastructure areas | Statement: [Distinguished Engineer at Google, impactScope, multiple products or infrastructure areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactScope Context triple: [Distinguished Engineer at Google, impactScope, multiple products or infrastructure areas]
-
A.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
B.
impactDescription
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
-
C.
impactOrigin
Indicates that one entity is the source or cause from which the impact or effect on another entity originates.
-
D.
impactLevel
Indicates the degree or intensity of effect that one entity, action, or event has on another.
-
E.
impactExperimentTarget
Indicates that an experiment directly affects, influences, or produces a measurable impact on a specified target.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671665bc8190b9b4a4ce4ec5b2eb |
completed | April 19, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69e478de85088190ba5f005f1d39f587 |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e484133ee48190a80f1889d79f34c9 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 11:49 a.m.