Triple
T13405740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | County of Tripoli |
E319951
|
entity |
| Predicate | includedTerritory |
P285
|
FINISHED |
| Object | Tortosa |
E235396
|
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: Tortosa | Statement: [County of Tripoli, includedTerritory, Tortosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tortosa Context triple: [County of Tripoli, includedTerritory, Tortosa]
-
A.
Tortosa
chosen
Tortosa is a historic city in Catalonia, Spain, known for its medieval architecture and strategic location near the mouth of the Ebro River.
-
B.
Martorell
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
-
C.
Denia
Denia is a coastal city on Spain’s Costa Blanca known for its historic castle, Mediterranean beaches, and vibrant port.
-
D.
Deià
Deià is a picturesque coastal village on the Spanish island of Mallorca, famed for its dramatic mountain-and-sea scenery and its long association with artists and writers.
-
E.
Tàrrega
Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae4be498819094798473a1bfd853 |
completed | April 12, 2026, 2:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7461b0dd481908719fc4657bb92bb |
completed | May 3, 2026, 12:56 p.m. |
Created at: April 9, 2026, 9:34 p.m.