Triple
T34775199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Vaidehī |
E1002482
|
entity |
| Predicate | associatedTextType |
P5468
|
FINISHED |
| Object | meditation sutra |
—
|
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: meditation sutra | Statement: [Queen Vaidehī, associatedTextType, meditation sutra]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedTextType Context triple: [Queen Vaidehī, associatedTextType, meditation sutra]
-
A.
associatedWithText
Indicates that an entity has a contextual or semantic connection to a specific piece of text.
-
B.
textType
chosen
Indicates the classification of a text according to its type, format, or genre.
-
C.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
D.
allyType
Indicates that one entity is classified as a specific type or category of ally in relation to another entity.
-
E.
ariaType
Indicates that one entity is classified as a specific type or category within the ARIA (Accessible Rich Internet Applications) specification.
- 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_69f76db30a108190bb57ca95b873e5bb |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fd7aac8190873077e63873aa72 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 3:59 p.m.