Triple
T13250330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haymana |
E315507
|
entity |
| Predicate | languageUsed |
P238
|
FINISHED |
| Object | Turkish |
E1028562
|
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: Turkish | Statement: [Haymana, languageUsed, Turkish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turkish Context triple: [Haymana, languageUsed, Turkish]
-
A.
Turkish
chosen
Turkish is a Turkic language primarily spoken in Turkey and Northern Cyprus, and by Turkish communities across Europe and the Middle East.
-
B.
TURKISH
TURKISH is the radio callsign used by Turkish Airlines, the national flag carrier airline of Turkey.
-
C.
Turk
Turk is a central character in the "Good Vibes" animated series, known for his laid-back surfer persona and comedic role in the show's coastal teen setting.
-
D.
Turk
Turk is an American rapper best known as a member of the New Orleans hip hop group the Hot Boys alongside Lil Wayne, Juvenile, and B.G.
-
E.
Turk
Turk is the affable, high-energy surgeon from the television series "Scrubs," known for his close friendship with J.D. and his comedic, dance-loving personality.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f71c5388190a6e122e14384efd7 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a3b8ca48190863aff25f12d0e7e |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:24 p.m.