Triple
T29211081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Civaka Cintamani Kaviyam |
E740546
|
entity |
| Predicate | hasTextualTransmission |
P183720
|
FINISHED |
| Object | palm-leaf manuscripts |
—
|
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: palm-leaf manuscripts | Statement: [Civaka Cintamani Kaviyam, hasTextualTransmission, palm-leaf manuscripts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTextualTransmission Context triple: [Civaka Cintamani Kaviyam, hasTextualTransmission, palm-leaf manuscripts]
-
A.
textualTransmission
Indicates the transfer or communication of information through written or printed text from one source to another.
-
B.
hasTextualImpact
Indicates that one entity influences, alters, or contributes to the content, style, or interpretation of another entity’s text.
-
C.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
-
D.
languageOfTransmission
Indicates the language used to convey or transmit the content or information in a given communication or resource.
-
E.
hasTextualCorpus
Indicates that an entity is associated with or possesses a collection of written or textual materials.
- 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_69f07cba2f808190a2746477d4e8345b |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f7a283388c81908e4a9ee3369e8d6f |
completed | May 3, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a224365081908ff6958e3b30bd05 |
completed | May 3, 2026, 7:29 p.m. |
Created at: April 28, 2026, 12:11 p.m.