Triple
T2422451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheridan Le Fanu |
E53447
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Carmilla |
E265099
|
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: Carmilla | Statement: [Sheridan Le Fanu, wrote, Carmilla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carmilla Context triple: [Sheridan Le Fanu, wrote, Carmilla]
-
A.
Carmilla
chosen
Carmilla is an 1872 Gothic novella by Sheridan Le Fanu that features one of literature’s earliest and most influential lesbian vampire characters.
-
B.
The Vampire
"The Vampire" is a film featuring actress Lydia Reed, known for her work in mid-20th-century American cinema.
-
C.
Vampirina
Vampirina is an animated Disney Junior television series that follows a young vampire girl adjusting to life in the human world after moving from Transylvania to Pennsylvania.
-
D.
Interview with the Vampire
Interview with the Vampire is a 1994 gothic horror film, based on Anne Rice’s novel, that follows a centuries-long story of immortal vampires and stars Brad Pitt and Tom Cruise.
-
E.
Dracula
Dracula is Bram Stoker’s 1897 Gothic horror novel that introduced the iconic vampire Count Dracula and helped define modern vampire fiction.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc971093481909c8924d58187860c |
completed | March 7, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0a299648190a55e9f2c47bd307f |
completed | March 9, 2026, 4:09 p.m. |
Created at: March 6, 2026, 9:42 p.m.