Triple
T10566214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Ross |
E249355
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Betsy |
E220459
|
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: Betsy | Statement: [Elizabeth Ross, nickname, Betsy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betsy Context triple: [Elizabeth Ross, nickname, Betsy]
-
A.
Betsy
chosen
Betsy is a common diminutive or nickname for the given name Elizabeth.
-
B.
Betsy
Betsy is a key female character in the 1976 film "Taxi Driver," known as the idealistic campaign worker who becomes the object of Travis Bickle’s fixation.
-
C.
Martha
Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
-
D.
Mary Ann
Mary Ann is the namesake of the city of Marianna in Florida.
-
E.
Mary Ann
"Mary Ann" is a song by the American punk rock band Squirtgun, recognized as one of their more notable tracks.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ce78c8190bf3227053ef88a48 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb055648c819085d36458847926b4 |
completed | April 14, 2026, 9:23 p.m. |
Created at: April 6, 2026, 12:36 p.m.