Triple
T2851613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tang Enbo |
E63103
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tang Enbo |
E63103
|
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: Tang Enbo | Statement: [Tang Enbo, name, Tang Enbo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tang Enbo Context triple: [Tang Enbo, name, Tang Enbo]
-
A.
Tang Enbo
chosen
Tang Enbo was a prominent Nationalist Chinese general known for his leadership in major battles against Japanese forces during the Second Sino-Japanese War.
-
B.
Peng Sanyuan
Peng Sanyuan is a Chinese film and television director and screenwriter best known for socially conscious dramas such as the film "Lost and Love."
-
C.
Peng Yuchang
Peng Yuchang is a Chinese actor and singer known for his roles in popular youth films and television dramas.
-
D.
Sun Lianzhong
Sun Lianzhong was a Nationalist Chinese general noted for his leadership in key battles against Japanese forces during the Second Sino-Japanese War.
-
E.
Yin Jichang
Yin Jichang is a Chinese sculptor best known for designing and creating the iconic Five Rams Statue in Guangzhou.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5ca2648190bd32c6ec4b0dd3b6 |
completed | March 7, 2026, 8:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f3ac3bc481909bf1d220fdf06488 |
completed | March 12, 2026, 5:11 p.m. |
Created at: March 6, 2026, 10:02 p.m.