Triple
T21967400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhengyi bu |
E542494
|
entity |
| Predicate | containsTextsFor |
P146732
|
FINISHED |
| Object | ritual performance |
—
|
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: ritual performance | Statement: [Zhengyi bu, containsTextsFor, ritual performance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsTextsFor Context triple: [Zhengyi bu, containsTextsFor, ritual performance]
-
A.
containsText
Indicates that one entity includes the specified text string within its content.
-
B.
hasTextBy
Indicates that one entity (such as a document, work, or record) contains or is associated with text authored or written by another entity.
-
C.
hasText
Indicates that an entity is associated with or contains a specific piece of textual content.
-
D.
hasCompanionInText
Indicates that one entity is accompanied by or associated with another entity within the same textual context or passage.
-
E.
usesTextBy
Indicates that one entity makes use of or relies on a text authored or provided by 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_69e0c47fab1081908dc74a6545dbb051 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1245c5d148190af2a06190ba32feb |
completed | April 28, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69e6f601f2188190893bcdde0cf58ad6 |
completed | April 21, 2026, 3:58 a.m. |
| PDg | Predicate description generation | batch_69e6fb9b75308190addc3dba7b5d5ddd |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 8:01 p.m.