Triple
T6375974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mid-South |
E143464
|
entity |
| Predicate | economicInfluenceFrom |
P8004
|
FINISHED |
| Object | Memphis logistics industry |
—
|
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: Memphis logistics industry | Statement: [Mid-South, economicInfluenceFrom, Memphis logistics industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicInfluenceFrom Context triple: [Mid-South, economicInfluenceFrom, Memphis logistics industry]
-
A.
hasSignificantInfluenceIn
Indicates that one entity exerts a substantial impact or shaping effect on another entity within a particular domain, context, or outcome.
-
B.
sectorInfluence
chosen
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
C.
typeOfInfluence
Indicates the specific nature or category of influence that one entity exerts on another.
-
D.
influentialFrom
Indicates that one entity has exerted influence on another, contributing to or shaping the latter’s ideas, behavior, or development.
-
E.
politicalInfluenceIn
Indicates that an entity exerts or holds political influence within a specified place, jurisdiction, or political context.
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0683bfc7081908b15c3c9a3c72e7b |
completed | March 22, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69c060ee055081908c79a1d151bd74cd |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:33 p.m.