Triple
T12674719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fleabag |
E302774
|
entity |
| Predicate | supportingCharacter |
P7748
|
FINISHED |
| Object | Claire |
E494299
|
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: Claire | Statement: [Fleabag, supportingCharacter, Claire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claire Context triple: [Fleabag, supportingCharacter, Claire]
-
A.
Claire
Claire is a feminine given name of French origin meaning "clear" or "bright," commonly used in English-speaking countries.
-
B.
Claire
chosen
Claire is a sharp-tongued, alcoholic sister whose acerbic wit and emotional volatility provide both dark humor and tension in Edward Albee’s play "A Delicate Balance."
-
C.
Claire Marie
Claire Marie was a small early 20th-century avant-garde publishing imprint associated with experimental modernist literature.
-
D.
Claire Louise
Claire Louise was the wife of American film actor Robert Armstrong, best known for his role in the classic movie "King Kong."
-
E.
Claire Bennett
Claire Bennett is the acerbic, grief-stricken woman living with chronic pain portrayed by Jennifer Aniston in the drama film "Cake."
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961af991c8190b6079cb57e593b8f |
completed | April 10, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a163188190b6077c77f9a81681 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:20 p.m.