Triple
T21941129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samambaia |
E541821
|
entity |
| Predicate | roadConnectionTo |
P9041
|
FINISHED |
| Object | Taguatinga |
—
|
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: Taguatinga | Statement: [Samambaia, roadConnectionTo, Taguatinga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taguatinga Context triple: [Samambaia, roadConnectionTo, Taguatinga]
-
A.
Taguatinga
chosen
Taguatinga is a major administrative region and commercial hub within Brazil’s Federal District, located near Brasília.
-
B.
Takashima
Takashima is a lakeside city in western Shiga Prefecture, Japan, known for its scenic location along Lake Biwa and surrounding mountains.
-
C.
Takashima
Takashima is a prominent commercial and waterfront district in Nishi-ku, Yokohama, known for major shopping complexes and modern urban development.
-
D.
Tobaku
Tobaku is a dialect of the Uma language spoken by an indigenous community in Central Sulawesi, Indonesia.
-
E.
Sayama
Sayama is a city in central Saitama Prefecture, Japan, known for its tea production and suburban residential character within the Greater Tokyo area.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12422245c8190af128b29e2d9c1fc |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:55 p.m.