Triple
T1459423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chía |
E31475
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Tenjo |
E37138
|
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: Tenjo | Statement: [Chía, borderedBy, Tenjo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tenjo Context triple: [Chía, borderedBy, Tenjo]
-
A.
Tenjo
chosen
Tenjo is a small municipality and town in the department of Cundinamarca, Colombia, known for its rural landscapes and proximity to Bogotá.
-
B.
Taihoku
Taihoku was the Japanese colonial-era name for Taipei, which served as the administrative and political center of Taiwan under Japanese rule.
-
C.
Moruya
Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
-
D.
Kanuma
Kanuma is a regional harvest festival celebrated mainly in Andhra Pradesh and Telangana as part of the multi-day Makar Sankranti festivities, focusing on cattle worship and agricultural prosperity.
-
E.
Tenjin
Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c59c1c288190be08064f2d351b2b |
completed | March 1, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1c9daa748190865d97f632ab8a37 |
completed | March 8, 2026, 6:52 a.m. |
Created at: March 1, 2026, 8 p.m.