Triple
T8407359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhou Yu |
E198531
|
entity |
| Predicate | commandSpecialty |
P44263
|
FINISHED |
| Object | riverine and naval operations |
—
|
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: riverine and naval operations | Statement: [Zhou Yu, commandSpecialty, riverine and naval operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commandSpecialty Context triple: [Zhou Yu, commandSpecialty, riverine and naval operations]
-
A.
unitSpecialization
Indicates that one unit is a specialized or more specific version of another unit within a hierarchical or categorical relationship.
-
B.
positionSpecialization
chosen
Indicates that one position is a more specialized or focused variant of another, broader position.
-
C.
uniformSpecialty
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
-
D.
teamSpecialty
Indicates the particular area of expertise or focus that characterizes a team’s skills or activities.
-
E.
escapeSpecialty
Indicates that one entity leaves, avoids, or breaks free from a particular specialized role, field, or area of expertise associated with 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb831409308190981089c303ebaef4 |
completed | March 31, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:05 p.m.