Triple
T2497987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dibang River |
E52395
|
entity |
| Predicate | hasFloodIssue |
P13044
|
FINISHED |
| Object | seasonal flooding in Assam |
—
|
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: seasonal flooding in Assam | Statement: [Dibang River, hasFloodIssue, seasonal flooding in Assam]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFloodIssue Context triple: [Dibang River, hasFloodIssue, seasonal flooding in Assam]
-
A.
hasFloodHistory
Indicates that the subject has experienced one or more flood events in the past.
-
B.
hasFloodRisk
Indicates that an entity is exposed to a potential or expected risk of flooding under certain conditions.
-
C.
hasFloodProtectionProject
Indicates that a flood protection project exists or is implemented for the referenced entity.
-
D.
hasFloodProtectionInfrastructure
Indicates that there exists built or implemented infrastructure designed to protect against or mitigate flooding for the referenced entity.
-
E.
hasSeasonalFlooding
chosen
Indicates that an area regularly experiences flooding during specific, recurring times of the year.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1ad2f8c81908853e97d75081e84 |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.