Triple
T13410810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anja C. Andersen |
E320080
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anja |
E657515
|
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: Anja | Statement: [Anja C. Andersen, givenName, Anja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anja Context triple: [Anja C. Andersen, givenName, Anja]
-
A.
Anja
chosen
Anja is a feminine given name commonly used in various European countries, often considered a variant of Anna.
-
B.
Anela
Anela is a small town and comune in the historical Logudoro region of northern Sardinia, Italy.
-
C.
Anika
Anika is the first name of Anika Noni Rose, an American actress and singer best known for voicing Tiana in Disney’s "The Princess and the Frog."
-
D.
Katja
Katja is a diminutive or short form of the given name Katarina, commonly used in various Slavic and European languages.
-
E.
Corina
Corina is a feminine given name used in various cultures, often considered a variant of names like Corine or Corinna.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb3facc819088c1af3b59237e7a |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7307ccff08190aa4037aa5a48f7d0 |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 9, 2026, 9:35 p.m.