Triple
T32458367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taishi |
E829493
|
entity |
| Predicate | eraSequenceInReign |
P174198
|
FINISHED |
| Object | first era of Emperor Wu of Han |
—
|
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: first era of Emperor Wu of Han | Statement: [Taishi, eraSequenceInReign, first era of Emperor Wu of Han]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eraSequenceInReign Context triple: [Taishi, eraSequenceInReign, first era of Emperor Wu of Han]
-
A.
eraSequenceWithinDynasty
Indicates that one historical era occurs in a specific ordered position relative to another era within the same dynasty.
-
B.
eraSequenceInEmperorLingReign
Indicates that one era directly follows another within the chronological sequence of Emperor Ling’s reign.
-
C.
chronologyWithinReignOf
Indicates that one event or time period occurs entirely within the span of a specified ruler’s reign.
-
D.
numberOfReigns
Indicates the count of distinct reigns or ruling periods associated with an entity.
-
E.
centuryOfReign
Indicates the century during which a given entity’s reign or rule took place.
- 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_69f3491df9288190afc0b23b1d6e72ce |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c31c7e888190840849dda3b90fd2 |
completed | May 3, 2026, 3:38 a.m. |
| PD | Predicate disambiguation | batch_69f6ba700a708190ab6db62791e43774 |
completed | May 3, 2026, 3:01 a.m. |
| PDg | Predicate description generation | batch_69f6bbbe23d48190b2aa662d69b41900 |
completed | May 3, 2026, 3:06 a.m. |
Created at: May 1, 2026, 12:56 a.m.