Triple
T1740242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Nile |
E38215
|
entity |
| Predicate | floodSeasonCause |
P31770
|
FINISHED |
| Object | Ethiopian monsoon rains |
—
|
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: Ethiopian monsoon rains | Statement: [Blue Nile, floodSeasonCause, Ethiopian monsoon rains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: floodSeasonCause Context triple: [Blue Nile, floodSeasonCause, Ethiopian monsoon rains]
-
A.
hasSeasonalFlooding
Indicates that an area regularly experiences flooding during specific, recurring times of the year.
-
B.
hasFloodRisk
Indicates that an entity is exposed to a potential or expected risk of flooding under certain conditions.
-
C.
hydrologicalProcess
Indicates a relationship where an entity participates in, is affected by, or is otherwise involved in the movement, distribution, or transformation of water within the hydrological cycle.
-
D.
hydrologyFeature
Indicates a relationship where one entity is a hydrological feature (such as a body or flow of water) associated with or characterizing another entity.
-
E.
flowsIntoBodyOfWaterType
Indicates that one body of water moves or drains into another body of water of a specified type (e.g., river, lake, ocean).
- F. None of above. chosen
Provenance (4 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab3c2559ac8190905186406fcaccb9 |
completed | March 6, 2026, 8:42 p.m. |
| PD | Predicate disambiguation | batch_69aa61c4023c819099cbe439aefda71f |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab3c2479148190badc616f8e2686d4 |
completed | March 6, 2026, 8:42 p.m. |
Created at: March 4, 2026, 7:30 p.m.