Triple
T23665635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xuanzang |
E584569
|
entity |
| Predicate | numberOfTextsBroughtBack |
P152907
|
FINISHED |
| Object | over 600 Sanskrit texts |
—
|
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: over 600 Sanskrit texts | Statement: [Xuanzang, numberOfTextsBroughtBack, over 600 Sanskrit texts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTextsBroughtBack Context triple: [Xuanzang, numberOfTextsBroughtBack, over 600 Sanskrit texts]
-
A.
numberOfTexts
Indicates the quantity or count of text messages associated with a given entity or interaction.
-
B.
broughtBack
Indicates that an entity caused another entity to return to a previous place, state, or condition.
-
C.
numberOfMainTexts
Indicates the quantity of primary or main textual components associated with an entity.
-
D.
numberOfScribes
Indicates the quantity of scribes associated with a given entity or context.
-
E.
hasRecordedTexts
Indicates that there exist written or otherwise recorded textual materials documenting or produced by the subject 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_69e24901421881908c17a5293bdd4a8e |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b40b8bd48190922c7252e71a5421 |
completed | April 29, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69f118dd13008190a8799b4e9cadbd79 |
completed | April 28, 2026, 8:30 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:50 p.m.