Triple
T16388167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selçuk Hatun |
E397977
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Hatun |
E447353
|
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: Hatun | Statement: [Selçuk Hatun, title, Hatun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatun Context triple: [Selçuk Hatun, title, Hatun]
-
A.
Hatun
chosen
Hatun is a historical Turkish honorific title traditionally used for noblewomen or ladies of high social status in Turkic and Ottoman societies.
-
B.
Hauya
Hauya is a small genus of flowering plants in the evening primrose family, native to mountainous regions of Central and South America.
-
C.
Harauti
Harauti is an Indo-Aryan dialect of the Rajasthani language spoken primarily in the Hadoti region of Rajasthan, India.
-
D.
Hunza
Hunza was the principal city and political center of the Zaque rulers within the pre-Columbian Muisca Confederation in what is now central Colombia.
-
E.
Hunza
Hunza is a mountainous valley and popular tourist destination in northern Pakistan, renowned for its dramatic Karakoram scenery and traditionally long-lived local population.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e3263f18988190800b921381d60c1b |
completed | April 18, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356ed47c819085aaf101459dd55c |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.