Triple
T14061919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yesügei |
E338366
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Qasar |
E569321
|
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: Qasar | Statement: [Yesügei, child, Qasar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Qasar Context triple: [Yesügei, child, Qasar]
-
A.
Qasar
chosen
Qasar was a prominent member of the Mongol Borjigin clan, known historically as a close kinsman and military supporter of Genghis Khan.
-
B.
Kasba
Kasba is a town in the Purnia district of Bihar, India, known as a local commercial and administrative center for surrounding rural areas.
-
C.
Saqba
Saqba is a town in southwestern Syria that forms part of the urban and suburban area surrounding the capital, Damascus.
-
D.
Kifri
Kifri is a town in Iraq with a significant Iraqi Turkmen population and cultural presence.
-
E.
Kadmat
Kadmat is a coral island in India’s Lakshadweep archipelago, known for its white-sand beaches, clear lagoons, and vibrant marine life that make it a popular destination for snorkeling and diving.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de568876308190840361dcaf10bd45 |
completed | April 14, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb6654850819083262f3fb981eb1a |
completed | May 7, 2026, 3:57 p.m. |
Created at: April 9, 2026, 10:21 p.m.