Triple
T4789289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chaim Weizmann |
E106562
|
entity |
| Predicate | wasBornIn |
P1
|
FINISHED |
| Object | Motal |
E97407
|
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: Motal | Statement: [Chaim Weizmann, wasBornIn, Motal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Motal Context triple: [Chaim Weizmann, wasBornIn, Motal]
-
A.
Motal
chosen
Motal is a small town in present-day Belarus, historically part of the Russian Empire, known as the birthplace of Israel’s first president, Chaim Weizmann.
-
B.
Mota
Mota is an Oceanic language of northern Vanuatu, historically notable as a regional lingua franca and early mission language in the area.
-
C.
Motala
Motala is a town in southern Sweden known for its location on Lake Vättern and its historic role as an industrial and canal hub.
-
D.
Mabor
Mabor is a tire brand owned by Continental AG, known for producing affordable passenger and commercial vehicle tires.
-
E.
Mosen
Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
- 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_69bd43f4a9588190bf73e20bc27c03cc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd65db847081908f5456724a2bdc65 |
completed | March 20, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be43e504488190b55cd745f82e9897 |
completed | March 21, 2026, 7:08 a.m. |
Created at: March 20, 2026, 1:22 p.m.