Triple
T7256709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King of Naples and Sicily |
E157740
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object | Naples |
E14805
|
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: Naples | Statement: [King of Naples and Sicily, hasCapital, Naples]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naples Context triple: [King of Naples and Sicily, hasCapital, Naples]
-
A.
Naples
chosen
Naples is a historic coastal city in southern Italy renowned for its rich cultural heritage, vibrant street life, and as the birthplace of pizza.
-
B.
Nápoles
Nápoles is a middle- to upper-class residential and commercial neighborhood in Mexico City, known for its modern architecture, the World Trade Center complex, and a mix of offices, restaurants, and apartment buildings.
-
C.
Neapoli
Neapoli is a small town in eastern Crete, Greece, known for its traditional architecture and role as a local administrative and cultural center.
-
D.
Palermo
Palermo is a large, upscale neighborhood in Buenos Aires known for its parks, nightlife, cultural attractions, and trendy dining and shopping areas.
-
E.
Palermo
Palermo is a 90 nm, low-power, budget-oriented core used in AMD's Sempron line of processors.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6eaa274bc8190b017b71583711453 |
completed | March 27, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7d3b541708190b66233813b167453 |
completed | March 28, 2026, 1:12 p.m. |
Created at: March 27, 2026, 2:57 p.m.