Triple
T19593951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krama patha |
E470303
|
entity |
| Predicate | hasExamplePattern |
P8151
|
FINISHED |
| Object | word1-word2, word2-word3, word3-word4 |
—
|
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: word1-word2, word2-word3, word3-word4 | Statement: [Krama patha, hasExamplePattern, word1-word2, word2-word3, word3-word4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExamplePattern Context triple: [Krama patha, hasExamplePattern, word1-word2, word2-word3, word3-word4]
-
A.
hasExampleType
Indicates that something is associated with a specific type or category of example that characterizes or illustrates it.
-
B.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
C.
hasPattern
chosen
Indicates that one entity exhibits, follows, or is characterized by a specific recurring form, structure, or design defined by another entity.
-
D.
hasUsePattern
Indicates a characteristic or recurring way in which something is typically used or applied.
-
E.
hasExampleImplementation
Indicates that an entity is accompanied by a concrete implementation that serves as an example of how it can be realized or used.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640782e2c8190b5baef07a2bdd015 |
completed | April 20, 2026, 3:04 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.