Triple
T13506911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hao Wang |
E321035
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hao |
E746749
|
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: Hao | Statement: [Hao Wang, givenName, Hao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hao Context triple: [Hao Wang, givenName, Hao]
-
A.
Hao
Hao is a large coral atoll in French Polynesia’s Tuamotu Archipelago, known historically as a strategic Pacific military and logistics base.
-
B.
Hao
chosen
Hao was an ancient Chinese city that served as an early capital of the Zhou dynasty.
-
C.
Hexie Hao
Hexie Hao is a series of high-speed electric multiple unit trains used in China’s railway network, known for operating many of the country’s major high-speed services.
-
D.
Huan
Huan is a given name most notably associated with the contemporary Chinese artist Zhang Huan, known for his performance and conceptual art.
-
E.
Hon Hai
Hon Hai, better known globally as Foxconn, is a Taiwanese multinational electronics manufacturer and the world’s largest contract producer of electronics devices.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf8259a08190ada13c4a3078f07d |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7548e51b881909a3384812556bc3d |
completed | May 3, 2026, 1:58 p.m. |
Created at: April 9, 2026, 9:43 p.m.