Triple
T15138733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sagunto |
E361624
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object | Saguntum |
E361624
|
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: Saguntum | Statement: [Sagunto, historicalName, Saguntum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saguntum Context triple: [Sagunto, historicalName, Saguntum]
-
A.
Sagunto
chosen
Sagunto is a historic coastal town in eastern Spain renowned for its ancient Roman theatre and hilltop castle overlooking the Mediterranean.
-
B.
Tarragona
Tarragona is a coastal municipality in the province of Davao Oriental on the southeastern island of Mindanao in the Philippines.
-
C.
Tarragona
Tarragona is a historic port city in northeastern Spain, renowned for its well-preserved Roman ruins and status as a major cultural and economic center in Catalonia.
-
D.
Altea
Altea is a picturesque coastal town on Spain’s Costa Blanca, known for its whitewashed old quarter, blue-domed church, and vibrant arts scene.
-
E.
Martorell
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005b59b488190b0016970647e7483 |
completed | April 15, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfec3ae48190b2d8e853dab00777 |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 3:07 a.m.