Triple
T13588024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gedung Batu Temple |
E324619
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Zheng He |
E186741
|
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: Zheng He | Statement: [Gedung Batu Temple, associatedWith, Zheng He]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zheng He Context triple: [Gedung Batu Temple, associatedWith, Zheng He]
-
A.
Zheng He
chosen
Zheng He was a famed 15th-century Chinese admiral and explorer who led vast maritime expeditions across the Indian Ocean during the Ming dynasty.
-
B.
Cheng Huan
Cheng Huan is the gentle Chinese immigrant protagonist of D. W. Griffith’s 1919 silent film "Broken Blossoms," whose idealism and compassion tragically collide with the brutality of his surroundings.
-
C.
Cristóvão da Gama
Cristóvão da Gama was a 16th-century Portuguese military commander and son of explorer Vasco da Gama, known for leading a doomed expedition in Ethiopia against Muslim forces.
-
D.
Zhenghe
Zhenghe was a Chinese imperial era name used during the reign of Emperor Huizong of the Song dynasty.
-
E.
Zhenghe
Zhenghe was an era name used during the reign of Emperor Wu of the Western Han dynasty in ancient China.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb054c6008190839384ce26e8f71a |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d3f80c081908710b46a42fb13a3 |
completed | May 3, 2026, 7:08 p.m. |
Created at: April 9, 2026, 9:49 p.m.