Triple
T37649514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baintha Brakk |
E937132
|
entity |
| Predicate | faceCharacteristics |
P188562
|
FINISHED |
| Object | very steep |
—
|
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: very steep | Statement: [Baintha Brakk, faceCharacteristics, very steep]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: faceCharacteristics Context triple: [Baintha Brakk, faceCharacteristics, very steep]
-
A.
facesChallenge
Indicates that an entity is confronted with a difficulty, obstacle, or demanding situation that must be dealt with or overcome.
-
B.
faceValueType
Indicates the type or category of a financial instrument’s face (nominal) value, such as how that value is defined or represented.
-
C.
hasFacialProfile
Indicates that an entity possesses a specific facial profile or configuration of facial features.
-
D.
hasFace
Indicates that one entity possesses, displays, or is characterized by a face.
-
E.
faceResemblance
Indicates that one entity’s facial appearance is similar to or resembles that of another entity.
- 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_69f76ed4fe908190b8061c5c135e0971 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbaa19e4a88190b04f26c0d4e708fd |
completed | May 6, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69fba8860f98819080b7bab05837b974 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbaa108ee48190b84d13df3ef3e365 |
completed | May 6, 2026, 8:52 p.m. |
Created at: May 3, 2026, 4:18 p.m.