Triple
T1501035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Mediterranean |
E29795
|
entity |
| Predicate | majorPort |
P528
|
FINISHED |
| Object | Valencia |
E13494
|
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: Valencia | Statement: [Western Mediterranean, majorPort, Valencia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valencia Context triple: [Western Mediterranean, majorPort, Valencia]
-
A.
Valencia
chosen
Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
-
B.
Valencia
Valencia is a major industrial and commercial city in north-central Venezuela and the capital of Carabobo state.
-
C.
Málaga
Málaga is a historic port city on Spain’s Costa del Sol, renowned for its Mediterranean beaches, rich Andalusian culture, and as the birthplace of artist Pablo Picasso.
-
D.
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.
-
E.
Lleida
Lleida is a historic city in western Catalonia, Spain, known for its medieval Seu Vella cathedral and role as a regional agricultural and commercial center.
- 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c716941c819080596b00e29999c7 |
completed | March 1, 2026, 11:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3b07b688190a0c90796da21c4d5 |
completed | March 8, 2026, 10:09 p.m. |
Created at: March 1, 2026, 8:12 p.m.