Triple
T8045799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sima Guang |
E187546
|
entity |
| Predicate | zizhiTongjianVolumes |
P2734
|
FINISHED |
| Object | 294 |
—
|
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: 294 | Statement: [Sima Guang, zizhiTongjianVolumes, 294]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: zizhiTongjianVolumes Context triple: [Sima Guang, zizhiTongjianVolumes, 294]
-
A.
numberOfVolumes
chosen
Indicates the total count of separate volumes or parts that make up a multi-volume work or collection.
-
B.
storageCapacity
Indicates the maximum amount of data or material that a storage entity can hold.
-
C.
spaceUsage
Indicates how much physical or storage space is occupied or utilized by an entity relative to the total available space.
-
D.
totalSpace
Indicates the overall amount of space or capacity available or occupied by an entity or within a given context.
-
E.
foundInVolume
Indicates that one entity is physically or logically contained within, or occurs in, a specific volume (such as a book volume, data volume, or bounded collection).
- 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f4d9ddc8190a7dcf85ed47ee6c3 |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049688208190b32088bd2c5930bc |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:24 p.m.