Triple
T22661411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ada Hegerberg |
E559672
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ada |
—
|
NE NERFINISHED |
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: Ada | Statement: [Ada Hegerberg, givenName, Ada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ada Context triple: [Ada Hegerberg, givenName, Ada]
-
A.
Ada
Ada is a town in northern Serbia's Vojvodina region, known for its Hungarian ethnic majority and location along the Tisa River.
-
B.
Ada
Ada is a small village in northwest Ohio, known as the home of Ohio Northern University.
-
C.
Ada
chosen
Ada is the given name of Ada Yonath, the Nobel Prize–winning Israeli crystallographer renowned for her pioneering work on the structure of the ribosome.
-
D.
Ada
Ada is a competitive boxer known for her performances in the ring.
-
E.
Ada
Ada is a statically typed, high-level programming language designed with strong support for reliability, safety, and real-time systems, widely used in aerospace, defense, and other mission-critical applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765fe7d081908087778c54c1e612 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:07 p.m.