Triple
T8850706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Du Fu |
E210629
|
entity |
| Predicate | era |
P200
|
FINISHED |
| Object | High Tang |
E184538
|
NE 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: High Tang | Statement: [Du Fu, era, High Tang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: High Tang Context triple: [Du Fu, era, High Tang]
-
A.
High Tang
chosen
High Tang refers to the flourishing middle period of China’s Tang dynasty, marked by political strength, territorial expansion, and a golden age of poetry and culture.
-
B.
High Coast
The High Coast is a dramatic coastal region in northeastern Sweden renowned for its steep cliffs, unique post-glacial rebound landscape, and UNESCO World Heritage status.
-
C.
Touchstone
Touchstone is a publishing imprint known for releasing a wide range of commercial fiction and nonfiction titles.
-
D.
Touchstone
Touchstone is the witty and sharp-tongued court jester in Shakespeare’s comedy "As You Like It," known for his clever wordplay and satirical commentary.
-
E.
Taranga
Taranga is a figure in Polynesian mythology known as the mother of the culture hero and demigod Māui.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca838a424c8190b1ecac115c2927e7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60c2300c819097b1ca6ebe2f749a |
completed | April 1, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf89cb853c8190a7664f2e7de0de87 |
completed | April 3, 2026, 9:35 a.m. |
Created at: March 30, 2026, 6:49 p.m.