Triple
T7211423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Thousand Hills: Rwanda’s Rebirth and the Man Who Dreamed It |
E149411
|
entity |
| Predicate | subjectConcept |
P531
|
FINISHED |
| Object | state-led development |
—
|
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: state-led development | Statement: [A Thousand Hills: Rwanda’s Rebirth and the Man Who Dreamed It, subjectConcept, state-led development]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectConcept Context triple: [A Thousand Hills: Rwanda’s Rebirth and the Man Who Dreamed It, subjectConcept, state-led development]
-
A.
categoryConcept
Indicates that one entity serves as a categorical type or conceptual class under which the other entity is grouped or classified.
-
B.
hasConcept
chosen
Indicates that an entity includes, embodies, or is associated with a particular concept.
-
C.
primaryConcept
Indicates that one concept is the main or central idea in relation to another concept or context.
-
D.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
E.
conceptualRole
Indicates the functional or semantic role that one concept plays in relation to another within a conceptual structure or system.
- 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_69c687eca814819095abb52316b1af80 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e96f99088190a085476bcfca26fd |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e75f84e481909e7866186ae80cff |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:53 p.m.