Triple
T7580116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobbi Kristina Brown |
E179463
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Bobbi |
E179463
|
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: Bobbi | Statement: [Bobbi Kristina Brown, givenName, Bobbi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bobbi Context triple: [Bobbi Kristina Brown, givenName, Bobbi]
-
A.
Bobbi
chosen
Bobbi is the given name of Bobbi Kristina Brown, the late daughter of singers Whitney Houston and Bobby Brown.
-
B.
Betty
Betty is the young, resourceful heroine of the children's story "Betty's Bright Idea," known for her cleverness and problem-solving nature.
-
C.
Betty
Betty is the familiar nickname of Betty Ford, the former First Lady of the United States and founder of the Betty Ford Center for substance abuse treatment.
-
D.
Betty
Betty is the birth name of iconic American actress Lauren Bacall, a legendary figure of Hollywood's Golden Age.
-
E.
Betty
"Betty" is the Allied reporting name for the Mitsubishi G4M, a Japanese World War II twin-engine land-based bomber known for its long range and vulnerability due to lack of armor and self-sealing fuel tanks.
- 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_69c69f327db881909a21ae3b156f8ded |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f975bbc08190aec30f902eaea494 |
completed | March 27, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c861735c7c81908e2c9fcbb1005ec3 |
completed | March 28, 2026, 11:17 p.m. |
Created at: March 27, 2026, 3:52 p.m.