Triple
T13070964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shivanasamudra Falls |
E329453
|
entity |
| Predicate | hasAverageHeight_m |
P37840
|
FINISHED |
| Object | about 90 |
—
|
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: about 90 | Statement: [Shivanasamudra Falls, hasAverageHeight_m, about 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAverageHeight_m Context triple: [Shivanasamudra Falls, hasAverageHeight_m, about 90]
-
A.
averageHeight
chosen
Indicates that the relationship specifies the mean height value calculated from a set of entities or measurements.
-
B.
averageStatureCharacteristic
Indicates that an entity has a stature (height or overall size) that is typical or average relative to a relevant comparison group.
-
C.
hasHeight
Indicates that one entity possesses a specific vertical measurement or stature.
-
D.
averageMaleHeight
Indicates the typical or mean height value associated with male individuals in a given population or context.
-
E.
estimatedHeadHeightInMeters
Indicates the estimated vertical height of a person's head, measured in meters.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ee6130819095d835e7ff6a8c5b |
completed | April 10, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9 p.m.