Triple
T4090180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 詹 |
E87686
|
entity |
| Predicate | romanization |
P2508
|
FINISHED |
| Object | Zhan |
E91243
|
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: Zhan | Statement: [詹, romanization, Zhan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhan Context triple: [詹, romanization, Zhan]
-
A.
Zhan
chosen
Zhan is a Chinese surname and given name represented in pinyin, corresponding to one of several Chinese characters and family lineages.
-
B.
Zhu
Zhu is a common Chinese surname borne by many notable historical and contemporary figures in China.
-
C.
Zeng
Zeng is a Chinese surname and given name commonly rendered in pinyin and borne by numerous historical and contemporary figures in China.
-
D.
Zhou
Zhou is a common Chinese surname borne by many notable figures in Chinese history and politics.
-
E.
Liang
Liang is a common Chinese surname borne by numerous historical figures, scholars, and public personalities across the Chinese-speaking world.
- 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_69aed94425148190be337845d56fac22 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcab0a1c8190a1b0ca48ebc95b31 |
completed | March 9, 2026, 5 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b6651d48190915581eca783cf3b |
completed | March 14, 2026, 2:06 p.m. |
Created at: March 9, 2026, 3:39 p.m.