Triple
T2199743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Smoke that Thunders |
E50460
|
entity |
| Predicate | typeOfWaterfall |
P36915
|
FINISHED |
| Object | curtain waterfall |
—
|
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: curtain waterfall | Statement: [The Smoke that Thunders, typeOfWaterfall, curtain waterfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfWaterfall Context triple: [The Smoke that Thunders, typeOfWaterfall, curtain waterfall]
-
A.
majorWaterfall
Indicates that a waterfall is of significant size, height, or volume relative to other waterfalls.
-
B.
hasWatercourseType
Indicates the specific kind or category of watercourse (such as river, stream, or canal) associated with an entity.
-
C.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
D.
waterbodyType
Indicates the classification of a water body according to its type (e.g., river, lake, ocean, etc.).
-
E.
nearbyWaterfall
Indicates that the entity is located close to or within a short distance of a waterfall.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf9e99f08190892d34485c8f2f25 |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda706f4819094de73e1d1d1f539 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf35c994819088a093c412931de4 |
completed | March 7, 2026, 6:01 a.m. |
Created at: March 4, 2026, 7:46 p.m.