Triple
T5921603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anita Borg |
E131710
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anita |
E162140
|
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: Anita | Statement: [Anita Borg, givenName, Anita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anita Context triple: [Anita Borg, givenName, Anita]
-
A.
Anita
chosen
Anita is a feminine given name used in various cultures, often as a diminutive of names like Ana or Anna.
-
B.
Marita
Marita is a feminine given name commonly used as a diminutive or affectionate form of the name Marie in various European languages.
-
C.
Trudy
Trudy is the nickname of Gertrude Ederle, the American competitive swimmer who became the first woman to swim across the English Channel.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
- 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03802ff4081908589236ba5cd196d |
completed | March 22, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c041d4f08190863141b037b1c05f |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.