Triple
T28173858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North America and Africa |
E715534
|
entity |
| Predicate | influenceEachOtherIn |
P133310
|
FINISHED |
| Object | migration patterns |
—
|
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: migration patterns | Statement: [North America and Africa, influenceEachOtherIn, migration patterns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influenceEachOtherIn Context triple: [North America and Africa, influenceEachOtherIn, migration patterns]
-
A.
influenceOf
Indicates that one entity affects, shapes, or alters the state, behavior, or properties of another entity.
-
B.
influencesThrough
Indicates that one entity affects or alters another entity indirectly by means of an intermediate factor, channel, or mechanism.
-
C.
interactionBetween
chosen
Indicates a reciprocal or mutual action, influence, or communication occurring between two or more entities.
-
D.
incorporatesInfluence
Indicates that one entity integrates or absorbs the influence, ideas, or characteristics of another into itself.
-
E.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
- 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_69efd6b340f0819095680e15dcdc1830 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f6cee45590819086e489bfccbe4ac3 |
completed | May 3, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1188708190b8f0f56e595e6057 |
completed | May 3, 2026, 4:16 a.m. |
Created at: April 27, 2026, 10:14 p.m.