Triple
T11086410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egypt–Sudan border |
E262130
|
entity |
| Predicate | hasSegmentDefinedInYear |
P75923
|
FINISHED |
| Object | 1899 |
—
|
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: 1899 | Statement: [Egypt–Sudan border, hasSegmentDefinedInYear, 1899]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSegmentDefinedInYear Context triple: [Egypt–Sudan border, hasSegmentDefinedInYear, 1899]
-
A.
hasSegmentOn
Indicates that one entity includes or occupies a specific segment or portion on another entity (such as a line, path, or sequence).
-
B.
hasAssociatedYear
chosen
Indicates that an entity is linked to a specific year that is relevant to it (e.g., creation, occurrence, or reference year).
-
C.
hasYearType
Indicates a relationship where an entity is associated with a specific classification or category of year (such as calendar, fiscal, academic, or other year type).
-
D.
hasOverrulingYear
Indicates the year in which a prior decision, rule, or action was formally overruled or invalidated.
-
E.
hasTypeOfYear
Indicates that a given year is classified as belonging to a specific type or category of year (e.g., fiscal, academic, leap).
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c3ed9c8190a3f5cdf1fe0e74a2 |
completed | April 9, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.