Triple
T16912449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fernando |
E410234
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Nando |
E292538
|
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: Nando | Statement: [Fernando, hasShortForm, Nando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nando Context triple: [Fernando, hasShortForm, Nando]
-
A.
Nando
chosen
Nando is a diminutive form of the given name Ferdinand, commonly used as a familiar or affectionate nickname.
-
B.
Nando's (restaurant)
Nando's is an international casual dining restaurant chain originating from South Africa, best known for its flame-grilled peri-peri chicken.
-
C.
Demel
Demel is a historic and prestigious Viennese pastry shop and chocolatier renowned for its traditional cakes and confections.
-
D.
Poultry Palace
Poultry Palace is a Pixar-themed quick-service restaurant at Disney California Adventure’s Pixar Pier, styled like a whimsical giant takeout box serving chicken-focused fare.
-
E.
Brisfit
Brisfit is the popular nickname for the Bristol F.2 Fighter, a highly successful British two-seat biplane used during World War I.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3ca3e6b9481909fbaeb0bddd7e3b2 |
completed | April 18, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7bb4ac481909318d3d61a2d10e1 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:30 a.m.