Triple
T13506867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jay Norwood "Ding" Darling |
E321034
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Ding |
E283722
|
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: Ding | Statement: [Jay Norwood "Ding" Darling, nickname, Ding]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ding Context triple: [Jay Norwood "Ding" Darling, nickname, Ding]
-
A.
Ding
chosen
Ding is a Chinese surname borne by various notable historical and contemporary figures.
-
B.
Dalingding
Dalingding is a barangay (village-level administrative division) of the municipality of Daanbantayan in the province of Cebu, Philippines.
-
C.
Dong
The Dong are an ethnic minority group in China, known for their distinctive wooden architecture, polyphonic folk singing, and concentration in the mountainous regions of southern China, including Guizhou Province.
-
D.
Sheng
Sheng is the primary male role type in traditional Chinese Peking opera, typically portraying dignified scholars, officials, and heroic figures.
-
E.
Sheng
Sheng is an urban Kenyan slang language that blends Swahili, English, and various local languages, widely spoken in Nairobi’s informal settlements and youth culture.
- 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.