Triple
T5034608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ibb |
E113391
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Green City
Green City is a lush, verdant urban area known for its abundant greenery and natural landscapes.
|
E488162
|
NE FINISHED |
How this triple was built (4 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: [Ibb, nickname, Green City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green City Context triple: [Ibb, nickname, Green City]
-
A.
Clean City
Clean City is a popular nickname for Rajshahi, a major city in western Bangladesh known for its cleanliness and greenery.
-
B.
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.
-
C.
Sustainability District
Sustainability District is one of Expo 2020 Dubai’s main themed zones, showcasing innovations, pavilions, and experiences focused on environmental stewardship and sustainable development.
-
D.
City of Gardens
The "City of Gardens" is a poetic nickname for Shiraz, a historic Iranian city renowned for its lush gardens, literary heritage, and cultural significance.
-
E.
City of Gardens
City of Gardens is a popular nickname for Lahore, highlighting its historic abundance of parks, green spaces, and Mughal-era gardens.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Green City Triple: [Ibb, nickname, Green City]
Generated description
Green City is a lush, verdant urban area known for its abundant greenery and natural landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Green City Target entity description: Green City is a lush, verdant urban area known for its abundant greenery and natural landscapes.
-
A.
Clean City
Clean City is a popular nickname for Rajshahi, a major city in western Bangladesh known for its cleanliness and greenery.
-
B.
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.
-
C.
Sustainability District
Sustainability District is one of Expo 2020 Dubai’s main themed zones, showcasing innovations, pavilions, and experiences focused on environmental stewardship and sustainable development.
-
D.
City of Gardens
The "City of Gardens" is a poetic nickname for Shiraz, a historic Iranian city renowned for its lush gardens, literary heritage, and cultural significance.
-
E.
City of Gardens
City of Gardens is a popular nickname for Lahore, highlighting its historic abundance of parks, green spaces, and Mughal-era gardens.
- F. None of above. chosen
Provenance (5 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b8646c8190b3cc20193e4639ee |
completed | March 20, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c759c608190875b6d48d99024b4 |
completed | March 21, 2026, 1:26 p.m. |
| NEDg | Description generation | batch_69be9de82ed48190bb85ba05a2d4ecbe |
completed | March 21, 2026, 1:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be9ea74c248190b08113606e43ebc5 |
completed | March 21, 2026, 1:35 p.m. |
Created at: March 20, 2026, 1:36 p.m.