Triple
T13040000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nallıhan |
E327166
|
entity |
| Predicate | inTimeZone |
P109
|
FINISHED |
| Object | TRT |
E30277
|
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: TRT | Statement: [Nallıhan, inTimeZone, TRT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TRT Context triple: [Nallıhan, inTimeZone, TRT]
-
A.
TRT
chosen
TRT is the time zone used in Turkey, corresponding to UTC+3.
-
B.
TRT
TRT is Turkey's national public broadcaster, operating multiple television and radio channels domestically and internationally.
-
C.
TRT
TRT is the station code for Tartu railway station, the main rail transport hub in the city of Tartu, Estonia.
-
D.
TRTS
TRTS is the commonly used abbreviation for the Taipei Metro rapid transit system serving the Taipei metropolitan area in Taiwan.
-
E.
TRTA
TRTA (Teito Rapid Transit Authority) was the former public corporation responsible for operating most of Tokyo’s subway network before its privatization into Tokyo Metro.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804d8e3081909584c93df099859a |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbd2f6a481909fdd418e7ad3cc22 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:55 p.m.