Triple
T16022173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Río Turia |
E388627
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Manises |
E350369
|
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: Manises | Statement: [Río Turia, flowsThrough, Manises]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manises Context triple: [Río Turia, flowsThrough, Manises]
-
A.
Manises
chosen
Manises is a town in Spain’s Valencian Community, known for its historic ceramics industry and proximity to Valencia.
-
B.
Binibeca
Binibeca is a picturesque coastal village on the Spanish island of Menorca, known for its whitewashed houses, narrow streets, and tranquil Mediterranean atmosphere.
-
C.
Guarao
Guarao is an indigenous language of the Warao people of the Orinoco Delta region in Venezuela.
-
D.
Mariveles
Mariveles is a coastal municipality at the southern tip of the Bataan Peninsula in the Philippines, known for its deep-water port, industrial zones, and role in World War II history.
-
E.
Biellese
Biellese refers to people or things originating from Biella, a city in the Piedmont region of northern Italy known for its textile and wool industry.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18323fc0881908bab7126d9ccf67d |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf2f7c4c8190b1290ac28aad8cd0 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:55 a.m.