Triple
T9707870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyrrhenian Sea coast |
E234944
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Livorno |
E67624
|
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: Livorno | Statement: [Tyrrhenian Sea coast, hasMajorCity, Livorno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Livorno Context triple: [Tyrrhenian Sea coast, hasMajorCity, Livorno]
-
A.
Livorno
chosen
Livorno is a port city on Italy’s western coast, historically notable for its diverse communities and significant Jewish population.
-
B.
La Spezia
La Spezia is a port city in northwestern Italy known as a major naval base and gateway to the Cinque Terre on the Ligurian coast.
-
C.
Savona
Savona is a coastal city and port in the Liguria region of northwestern Italy, known historically as a strategic maritime center on the Italian Riviera.
-
D.
Génova
Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
-
E.
Viareggio
Viareggio is a coastal city in Tuscany, Italy, renowned for its seaside resorts and famous annual Carnival.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9da5e09081909456909d768611e6 |
completed | April 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc3dd210819094403fd21f3c388d |
completed | April 5, 2026, 2:43 a.m. |
Created at: March 30, 2026, 8:19 p.m.