Triple
T17107749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bey |
E415143
|
entity |
| Predicate | contrastedWith |
P278
|
FINISHED |
| Object | Han |
E428478
|
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: Han | Statement: [Bey, contrastedWith, Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Han Context triple: [Bey, contrastedWith, Han]
-
A.
Han
chosen
Han refers to the majority ethnic group in China, historically associated with Chinese civilization, language, and culture.
-
B.
Han
Han is a common transliteration of the historical Central Asian title "Khan," often associated with rulers and nobility in various Turkic and Mongolic cultures.
-
C.
Hal
Hal is a masculine given name, commonly used as a diminutive form of Harold.
-
D.
Hannen
Hannen is an English surname associated with several notable figures, including actors and judges, in British history.
-
E.
Haan
Haan is a town in the German state of North Rhine-Westphalia, known for its location between Düsseldorf and Wuppertal and its mix of residential areas and light industry.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc280b0c8190b9e620b90e0d4b40 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a019540819083ce6100b24f8cfb |
completed | May 11, 2026, 2:08 a.m. |
Created at: April 10, 2026, 5:35 a.m.