Triple
T18664331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Dartmouth |
E456287
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
City of Lakes
City of Lakes is the nickname for Dartmouth, Nova Scotia, highlighting its numerous surrounding lakes and waterfronts.
|
E1336431
|
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: City of Lakes | Statement: [City of Dartmouth, hasNickname, City of Lakes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Lakes Context triple: [City of Dartmouth, hasNickname, City of Lakes]
-
A.
City of Lakes
City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
-
B.
City of Lakes
City of Lakes is a popular nickname for Udaipur, a picturesque city in Rajasthan, India, renowned for its numerous interconnected lakes and romantic waterfront scenery.
-
C.
City of Lakes
City of Lakes is a popular nickname for Thane, a city in Maharashtra, India, known for its numerous lakes and scenic waterfronts.
-
D.
City of Seven Lakes
City of Seven Lakes is the nickname of San Pablo, a city in the Philippines renowned for its seven crater lakes and scenic natural surroundings.
-
E.
Arbor Lakes
Arbor Lakes is a major retail and entertainment district in Maple Grove, Minnesota, featuring a mix of shops, restaurants, and commercial developments.
- 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: City of Lakes Triple: [City of Dartmouth, hasNickname, City of Lakes]
Generated description
City of Lakes is the nickname for Dartmouth, Nova Scotia, highlighting its numerous surrounding lakes and waterfronts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: City of Lakes Target entity description: City of Lakes is the nickname for Dartmouth, Nova Scotia, highlighting its numerous surrounding lakes and waterfronts.
-
A.
City of Lakes
City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
-
B.
City of Lakes
City of Lakes is a popular nickname for Udaipur, a picturesque city in Rajasthan, India, renowned for its numerous interconnected lakes and romantic waterfront scenery.
-
C.
City of Lakes
City of Lakes is a popular nickname for Thane, a city in Maharashtra, India, known for its numerous lakes and scenic waterfronts.
-
D.
City of Seven Lakes
City of Seven Lakes is the nickname of San Pablo, a city in the Philippines renowned for its seven crater lakes and scenic natural surroundings.
-
E.
Arbor Lakes
Arbor Lakes is a major retail and entertainment district in Maple Grove, Minnesota, featuring a mix of shops, restaurants, and commercial developments.
- 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_69d8d38f72b4819090a935175d9ca8af |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5508d2d588190a468bd7b205d2057 |
completed | April 19, 2026, 10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a051728ac508190a0c2640e8102ebb6 |
completed | May 14, 2026, 12:28 a.m. |
| NEDg | Description generation | batch_6a05196f9d788190a239274167986a00 |
completed | May 14, 2026, 12:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a051abf2e2c81909c5baca205235ce8 |
completed | May 14, 2026, 12:43 a.m. |
Created at: April 10, 2026, 11:48 a.m.