Triple
T16186441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zelenograd |
E392816
|
entity |
| Predicate | nameMeaning |
P453
|
FINISHED |
| Object | Green City |
E488162
|
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: Green City | Statement: [Zelenograd, nameMeaning, Green City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green City Context triple: [Zelenograd, nameMeaning, Green City]
-
A.
Green City
chosen
Green City is a lush, verdant urban area known for its abundant greenery and natural landscapes.
-
B.
Clean City
Clean City is a popular nickname for Rajshahi, a major city in western Bangladesh known for its cleanliness and greenery.
-
C.
Thrive City
Thrive City is the mixed-use entertainment and retail district surrounding San Francisco’s Chase Center, featuring restaurants, shops, and public gathering spaces for events and community activities.
-
D.
Circular City
Circular City is a historic walled island-terrace within Beijing’s Beihai Park, known for its ancient pavilions, stone carvings, and scenic views over the surrounding lake.
-
E.
Green City in the Sun
Green City in the Sun is a popular nickname for Nairobi, highlighting the Kenyan capital’s lush greenery and warm, sunny climate.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e22061f47481909ededd5eed40f5a4 |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0550b48190ac84946b7254552b |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.