Triple
T12709278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tang Enbo |
E303671
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Enbo |
E303671
|
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: Enbo | Statement: [Tang Enbo, givenName, Enbo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Enbo Context triple: [Tang Enbo, givenName, Enbo]
-
A.
Enbo
chosen
Enbo is a given name most notably associated with Tang Enbo, a prominent Chinese Nationalist general during the Second Sino-Japanese War and Chinese Civil War.
-
B.
Jibowu
Jibowu is a busy urban district in Lagos, Nigeria, known as a major transport hub with numerous intercity bus terminals and easy access to key city routes.
-
C.
Boyi
Boyi is the courtesy name of Jiang Wei, a prominent military general and strategist of the Shu Han state during China’s Three Kingdoms period.
-
D.
Surobi
Surobi is a town and district in eastern Afghanistan, strategically located along the Kabul River and a key site for nearby hydroelectric infrastructure.
-
E.
Koshun
Koshun is a music producer known for working on projects associated with the artist Amala.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96207b2d881908314efc3e350aa78 |
completed | April 10, 2026, 8:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671b648f48190a29924c484713fc7 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:23 p.m.