Triple
T15094411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Nice 1962 |
E360501
|
entity |
| Predicate | endLocation |
P37184
|
FINISHED |
| Object | Nice |
E2387
|
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: Nice | Statement: [Paris–Nice 1962, endLocation, Nice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nice Context triple: [Paris–Nice 1962, endLocation, Nice]
-
A.
Nice
chosen
Nice is a prominent Mediterranean coastal city on the French Riviera, known for its mild climate, beaches, and vibrant cultural life.
-
B.
Nice
Nice is a cabin class offered by Breeze Airways that provides a standard, budget-friendly economy experience for passengers.
-
C.
Pleasant
Pleasant is a given name historically used in English-speaking countries, notably borne by figures such as Pleasant Hannibal Clemens.
-
D.
Neat
Neat is a one-woman play by Charlayne Woodard that explores family, memory, and coming-of-age through the story of her developmentally disabled aunt.
-
E.
Nice To Have
"Nice To Have" is a song by American rapper and singer Danielle Balbuena, known professionally as 070 Shake, showcasing her emotive, genre-blending style.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0054571a48190a57055c0d6e90f82 |
completed | April 15, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feae21134c81908939ad6ce46703d8 |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 3:04 a.m.