Triple
T3903394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toulouse |
E90547
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Pink City |
E90547
|
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: Pink City | Statement: [Toulouse, hasNickname, Pink City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pink City Context triple: [Toulouse, hasNickname, Pink City]
-
A.
Pink City
chosen
Pink City is the popular nickname for the French city of Toulouse, known for its distinctive rose-hued brick architecture.
-
B.
Pink City
Pink City is the popular nickname for Jaipur, the capital of Rajasthan in India, famed for its distinctive rose-colored architecture and historic palaces.
-
C.
Bell City
Bell City is a nickname for Bristol, Connecticut, historically known for its prominent clock and bell manufacturing industry.
-
D.
The Queen City
The Queen City is a nickname for Plainfield, New Jersey, reflecting its historic prominence and once-elegant, prosperous character among regional cities.
-
E.
Rose City
Rose City is the nickname of Welland, Ontario, a Canadian city historically known for its abundance of roses and floral beauty.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecf5a7188190a1c722ec83e3c6cd |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51ca7636081908f98c4e22617f808 |
completed | March 14, 2026, 8:30 a.m. |
Created at: March 9, 2026, 3:21 p.m.