Triple
T18331211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adalet Ağaoğlu |
E439144
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Nallıhan |
—
|
NE NERFINISHED |
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: Nallıhan | Statement: [Adalet Ağaoğlu, placeOfBirth, Nallıhan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nallıhan Context triple: [Adalet Ağaoğlu, placeOfBirth, Nallıhan]
-
A.
Nallıhan
chosen
Nallıhan is a district and town in Turkey known for its natural landscapes, including colorful rock formations and rich birdlife, located within Ankara Province.
-
B.
Karaköy
Karaköy is a historic waterfront neighborhood in Istanbul known for its bustling port, cafes, and mix of traditional and modern urban life.
-
C.
Reyhanlı
Reyhanlı is a town and district in southern Turkey near the Syrian border, known for its strategic location and cross-border trade and tensions.
-
D.
Yazıhan
Yazıhan is a rural district and town in eastern Turkey known for its agricultural activities within Malatya Province.
-
E.
Güzelyurt
Güzelyurt is a town in the northwestern part of Cyprus, known for its citrus orchards and archaeological sites.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9175fec8190af865699b4e64d8c |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50eca08388190b4e757a5f63b5d41 |
completed | April 19, 2026, 5:20 p.m. |
Created at: April 10, 2026, 10:36 a.m.