Triple
T2991759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carle Van Loo |
E80770
|
entity |
| Predicate | birthPlace |
P1
|
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: [Carle Van Loo, birthPlace, Nice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nice Context triple: [Carle Van Loo, birthPlace, 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.
Nice Agreement
The Nice Agreement is an international treaty that establishes a standardized classification system of goods and services used for the registration of trademarks.
-
D.
FRIENDLY
FRIENDLY is the airline callsign used by Southern Airways Express, a U.S.-based commuter and regional airline.
-
E.
Nice Classification
Nice Classification is an international system that categorizes goods and services into standardized classes for the registration of trademarks.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99df69d08190a0e25efb0dc8d653 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b109039cfc8190a286c83df752967e |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:59 p.m.