Triple
T16126732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taikazan |
E391290
|
entity |
| Predicate | fieldRelation |
P6979
|
FINISHED |
| Object | fixed point of reference on Kok-boru field |
—
|
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: fixed point of reference on Kok-boru field | Statement: [Taikazan, fieldRelation, fixed point of reference on Kok-boru field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldRelation Context triple: [Taikazan, fieldRelation, fixed point of reference on Kok-boru field]
-
A.
foreignRelation
Indicates that there exists a diplomatic or international relationship between one entity and another entity from a different country or jurisdiction.
-
B.
valueRelation
Indicates a comparative or associative relationship between the values or magnitudes of two or more entities.
-
C.
relatedField
chosen
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
D.
datumRelation
Indicates a relationship where one piece of data is connected to, derived from, or otherwise associated with another piece of data.
-
E.
subjectRelation
Indicates that one entity stands in a specified relational role or connection to another entity.
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e20204fb408190b58d49d0d64bb740 |
completed | April 17, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69e1828518c48190a8ef3aaa46a1f639 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5 a.m.