Triple
T14096417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harsusi language |
E339263
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Harsusi |
E347905
|
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: Harsusi | Statement: [Harsusi language, hasAlternativeName, Harsusi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harsusi Context triple: [Harsusi language, hasAlternativeName, Harsusi]
-
A.
Harsusi
chosen
Harsusi is a critically endangered South Semitic language spoken by a small community in the Dhofar region of Oman.
-
B.
Dausa
Dausa is a town and district headquarters in the Indian state of Rajasthan, known for its historical forts, stepwells, and proximity to Jaipur.
-
C.
Uherka
Uherka is a river in eastern Poland that serves as a tributary of the Western Bug, flowing through the Lublin region.
-
D.
Kharp
Kharp is a remote settlement in Russia’s Yamalo-Nenets Autonomous Okrug, known for hosting the high-security IK-3 “Polar Wolf” penal colony.
-
E.
Horki
Horki is a town in eastern Belarus known for its agricultural academy and regional administrative significance.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fb926288190a7f0f50d1d585d76 |
completed | April 14, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0ac02488190b2114eb63ec604c3 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:22 p.m.