Triple
T23122891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sediq |
E576947
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Toda |
—
|
NE NERFINISHED |
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: Toda | Statement: [Sediq, hasDialect, Toda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toda Context triple: [Sediq, hasDialect, Toda]
-
A.
Toda
Toda is a city in Saitama Prefecture, Japan, located just north of Tokyo and known as a residential and commuter town in the Greater Tokyo Area.
-
B.
Toda
chosen
Toda is a subgroup of the Seediq, an Indigenous people of Taiwan known for their distinct language and cultural traditions.
-
C.
Toda
Toda is a Southern Dravidian language spoken by the Toda people of the Nilgiri Hills in southern India, known for its highly complex phonology and small speaker population.
-
D.
Tuhala
Tuhala is a small village in northern Estonia known for its karst landscapes and the famous Tuhala Witch’s Well, which periodically overflows in a striking natural phenomenon.
-
E.
Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e517a0481909829a73fdf255d1c |
completed | April 29, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:59 p.m.