Triple
T4031143
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lu Lingzi |
E83712
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lingzi |
E83712
|
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: Lingzi | Statement: [Lu Lingzi, givenName, Lingzi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lingzi Context triple: [Lu Lingzi, givenName, Lingzi]
-
A.
Lu Lingzi
chosen
Lu Lingzi was a Chinese graduate student at Boston University who was killed in the 2013 Boston Marathon bombing.
-
B.
Zhenyuan
Zhenyuan was a late 19th-century Chinese ironclad battleship of the Beiyang Fleet that played a prominent role in the First Sino-Japanese War.
-
C.
Shaoqi
Shaoqi is the given name of Liu Shaoqi, a prominent Chinese revolutionary leader and former President of the People’s Republic of China.
-
D.
Xiaozong
Xiaozong is the temple name of the Hongzhi Emperor, a Ming dynasty ruler noted for his relatively peaceful and reform-minded reign in late 15th-century China.
-
E.
Yuanhong
Yuanhong is a Chinese given name that appears in the full name of the historical figure Li Yuanhong.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb0dbb8481909ff2ee49dadcd1dc |
completed | March 9, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5629748d88190984cce08eef05e9c |
completed | March 14, 2026, 1:28 p.m. |
Created at: March 9, 2026, 3:36 p.m.