Triple
T3407079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selina Meyer |
E71799
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Selina |
E289144
|
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: Selina | Statement: [Selina Meyer, givenName, Selina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Selina Context triple: [Selina Meyer, givenName, Selina]
-
A.
Selina
chosen
Selina is a feminine given name of Latin origin, commonly used in English-speaking countries.
-
B.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
C.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
D.
Felicia
Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
-
E.
Gwen
Gwen is the Allied reporting name for the Mitsubishi Ki-21, a Japanese twin-engine bomber used extensively during World War II.
- 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_69ad85ac312481909e7027ced1456a9f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8ede9c48190b13b0f5e7474e7fa |
completed | March 8, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bdaf06c8190a8102a4e3c728066 |
completed | March 12, 2026, 11:27 p.m. |
Created at: March 8, 2026, 3:15 p.m.