Triple
T9008826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grândola |
E215413
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Sines |
E47673
|
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: Sines | Statement: [Grândola, locatedNear, Sines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sines Context triple: [Grândola, locatedNear, Sines]
-
A.
Sines
chosen
Sines is a coastal town in Portugal known as the birthplace of the famed explorer Vasco da Gama.
-
B.
SIN
SIN is the IATA airport code for Singapore Changi Airport, the main international gateway to Singapore and one of the world’s busiest and most acclaimed airports.
-
C.
Sinn
Sinn is a river in northern Bavaria, Germany, that flows through the Lower Franconia region.
-
D.
Sine
Sine was a precolonial Serer kingdom in what is now Senegal, known for its rich Serer culture, religious traditions, and resistance to Islamic and later French expansion.
-
E.
Sonning
Sonning is a picturesque village on the River Thames in Berkshire, England, known for its historic architecture, scenic riverside setting, and literary associations.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69bed8588190afc9cbca12b75a3b |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfd0ec84348190944a2310a6a60448 |
completed | April 3, 2026, 2:38 p.m. |
Created at: March 30, 2026, 7:06 p.m.