Triple
T11235618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ginger |
E265933
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Babs |
unclear NED1
|
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: Babs | Statement: [Ginger, associatedWith, Babs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Babs Context triple: [Ginger, associatedWith, Babs]
-
A.
Babs
Babs is the central protagonist of the work "In Fabric," around whom the film’s eerie and surreal events unfold.
-
B.
Babs
Babs is a fictional character from the 1970s American sitcom "Blansky's Beauties," which followed the lives of Las Vegas showgirls and their manager.
-
C.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Mabel
Mabel is a feminine given name of medieval origin that has been used in various English-speaking cultures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e904cf888190826fc964f76b5cb2 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ad6308f8819085652d6c529ac821 |
completed | April 19, 2026, 10:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.