Triple
T2556989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jens Bergensten |
E56750
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jens |
E126261
|
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: Jens | Statement: [Jens Bergensten, givenName, Jens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jens Context triple: [Jens Bergensten, givenName, Jens]
-
A.
Jens
chosen
Jens is a masculine given name commonly used in Scandinavian and German-speaking countries, equivalent to "John" in English.
-
B.
Johan
Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
-
C.
Niels
Niels is the given name of the pioneering Norwegian mathematician Niels Henrik Abel, known for his foundational work in algebra and analysis.
-
D.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
E.
Jens Jensen
Jens Jensen was a prominent Danish-American landscape architect known for his naturalistic designs and influential work on parks and estates in the American Midwest.
- 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd310b3a48190b275be13eb050e57 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d1d937c8190ac6b7bb405a49c3c |
completed | March 9, 2026, 11:51 p.m. |
Created at: March 6, 2026, 9:48 p.m.