Triple
T14920196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fariborz Maseeh |
E371487
|
entity |
| Predicate | hasNotableAreaOfImpact |
P74556
|
FINISHED |
| Object | engineering education infrastructure |
—
|
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: engineering education infrastructure | Statement: [Fariborz Maseeh, hasNotableAreaOfImpact, engineering education infrastructure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableAreaOfImpact Context triple: [Fariborz Maseeh, hasNotableAreaOfImpact, engineering education infrastructure]
-
A.
hasNotableImpact
Indicates that one entity exerts a significant or noteworthy influence or effect on another entity or context.
-
B.
hasLocalImpact
Indicates that an entity produces effects or consequences within a specific local area or community.
-
C.
hasImpactFocus
chosen
Indicates that an entity is primarily concerned with or directed toward a particular type or area of impact.
-
D.
hasCanonicalImpactOn
Indicates that one entity exerts a standard, authoritative, or officially recognized influence or effect on another entity.
-
E.
hasImpactScale
Indicates the degree or magnitude of impact that one entity or action has on another, typically expressed along a defined scale.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded62f76bc81909ebc8899096cd1a0 |
completed | April 15, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69de9a52ba988190a26e268b4ea083ea |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:33 a.m.