Triple
T14272387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange wine region |
E353821
|
entity |
| Predicate | grapeVariety |
P975
|
FINISHED |
| Object | Shiraz |
E38586
|
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: Shiraz | Statement: [Orange wine region, grapeVariety, Shiraz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiraz Context triple: [Orange wine region, grapeVariety, Shiraz]
-
A.
Shiraz
chosen
Shiraz is a dark-skinned wine grape variety, also known as Syrah, widely used to produce full-bodied red wines around the world.
-
B.
Shiraz, Iran
Shiraz, Iran is a historic city in southwestern Iran renowned for its rich Persian cultural heritage, poetry, gardens, and wine-making tradition.
-
C.
شیراز
شیراز یکی از مهمترین و تاریخیترین شهرهای ایران است که بهعنوان مرکز فرهنگ، ادب و شعر فارسی شناخته میشود.
-
D.
Shirazi
The Shirazi are a coastal East African ethnic group of mixed African and Persian ancestry, historically influential in the culture and Islamization of regions like Zanzibar.
-
E.
Kashan
Kashan is an Iranian city renowned for its rich history, traditional architecture, and production of high-quality Persian carpets.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de65811d7c8190b075909a6570d415 |
completed | April 14, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d16bae881909b38ccf04f1cf823 |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 10, 2026, 1:10 a.m.