Triple
T5401903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sevilla FC |
E120796
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Sevilla |
E359506
|
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: Sevilla | Statement: [Sevilla FC, shortName, Sevilla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sevilla Context triple: [Sevilla FC, shortName, Sevilla]
-
A.
Malaga
Malaga is a white wine grape variety name historically used as a synonym for Sémillon in certain wine-growing regions.
-
B.
Seville
chosen
Seville is a historic Spanish city in Andalusia renowned for its rich Moorish and Christian heritage, iconic landmarks like the Giralda and Alcázar, and vibrant cultural traditions such as flamenco.
-
C.
Seville
Seville is a small unincorporated rural community located in Volusia County, Florida, known for its agricultural surroundings and historic character.
-
D.
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.
-
E.
Valencia
Valencia is a municipality in the Philippine province of Negros Oriental known for its cool climate, geothermal energy resources, and natural attractions such as waterfalls and mountain landscapes.
- 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_69bd46391c0c81909fa484446732b6a3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8771b25c819080da247bc3164cd9 |
completed | March 20, 2026, 5:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf91ec7b108190a51bbe8aafd08bd2 |
completed | March 22, 2026, 6:53 a.m. |
Created at: March 20, 2026, 2:04 p.m.