Triple
T3801927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oghuz |
E91707
|
entity |
| Predicate | hasDescendant |
P3654
|
FINISHED |
| Object | Salar |
E103032
|
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: Salar | Statement: [Oghuz, hasDescendant, Salar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salar Context triple: [Oghuz, hasDescendant, Salar]
-
A.
Salar
chosen
Salar is a Turkic ethnic group primarily residing in northwestern China, known for speaking the Salar language and practicing Islam.
-
B.
Khora
Khora is one of the now nearly extinct indigenous languages once spoken by the Great Andamanese people of the Andaman Islands in India.
-
C.
Kalsa
Kalsa is a historic district of Palermo, Italy, known for its Arab-Norman heritage, medieval streets, and vibrant cultural life.
-
D.
Ḥimṣ
Ḥimṣ is an alternative transliteration of Homs, a major city in western Syria known for its historical significance and role in the Syrian conflict.
-
E.
Taba
Taba is a small Egyptian resort town on the Red Sea near the border with Israel, known for its beaches, coral reefs, and role as a popular gateway between the two countries.
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee7b998c08190b252178cd7436951 |
completed | March 9, 2026, 3:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f066e30481909e5baa630f3539e4 |
completed | March 14, 2026, 5:21 a.m. |
Created at: March 9, 2026, 3:15 p.m.