Triple
T12521842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pas de Peyrol |
E299336
|
entity |
| Predicate | roadAccessFrom |
P22549
|
FINISHED |
| Object | Murat |
E301498
|
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: Murat | Statement: [Pas de Peyrol, roadAccessFrom, Murat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Murat Context triple: [Pas de Peyrol, roadAccessFrom, Murat]
-
A.
Murat
chosen
Murat is a historic small town in south-central France, known for its volcanic landscape setting in the Cantal region and its traditional stone architecture.
-
B.
Gaziosmanpaşa
Gaziosmanpaşa is a densely populated residential and commercial district on the European side of Istanbul, known for its rapid urbanization and diverse working- and middle-class communities.
-
C.
Mehmet
Mehmet is a common Turkish male given name of Arabic origin, widely used across Turkey and among Turkish communities.
-
D.
Murad
Murad is a masculine given name of Arabic origin commonly used in various Muslim-majority cultures.
-
E.
Mahmut
Mahmut is a masculine given name commonly used in Turkish and related cultures, derived from the Arabic name Mahmoud.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545b2b2481909049a490c97678f2 |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bbf43e08190ae79f92ed5882ce2 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:57 p.m.