Triple
T7867597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Envigado |
E182655
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Itagüí |
E179160
|
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: Itagüí | Statement: [Envigado, hasNeighbour, Itagüí]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Itagüí Context triple: [Envigado, hasNeighbour, Itagüí]
-
A.
Itagüí
chosen
Itagüí is a densely populated industrial and commercial city in northwestern Colombia, located in the metropolitan area of Medellín.
-
B.
Minoh
Minoh is a suburban city in northern Osaka Prefecture, Japan, known for its scenic Minoh Waterfall, autumn foliage, and residential communities.
-
C.
Akishima
Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
-
D.
Suwa City
Suwa City is a regional city in central Japan known for its scenic Lake Suwa, hot springs, precision manufacturing industry, and the historic Suwa Taisha shrine complex.
-
E.
Fuji City
Fuji City is an industrial city in Shizuoka Prefecture, Japan, known for its paper manufacturing industry and views of nearby Mount Fuji.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3847c7fc819098e32b6548943da7 |
completed | March 31, 2026, 2:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b5908a88190bc00f0d6dbde0b58 |
completed | March 31, 2026, 5:27 a.m. |
Created at: March 30, 2026, 4:55 p.m.