Triple
T5128052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antony and Cleopatra |
E115629
|
entity |
| Predicate | containsCharacter |
P5716
|
FINISHED |
| Object | Charmian |
E496758
|
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: Charmian | Statement: [Antony and Cleopatra, containsCharacter, Charmian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charmian Context triple: [Antony and Cleopatra, containsCharacter, Charmian]
-
A.
Charmian
chosen
Charmian is a loyal and witty attendant to Cleopatra in William Shakespeare's tragedy "Antony and Cleopatra."
-
B.
Thisbe
Thisbe is a tragic heroine from classical mythology, best known from the tale of Pyramus and Thisbe, whose doomed love story inspired later works like Shakespeare’s "Romeo and Juliet."
-
C.
Daphne
Daphne is a nymph from Greek mythology best known for being pursued by Apollo and transformed into a laurel tree to escape him.
-
D.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
-
E.
Daphne
Daphne is a coastal city in Baldwin County, Alabama, situated along the eastern shore of Mobile Bay.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd782428a081909583c7f368226daf |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfd90a848190a1c78063a437cf3c |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:42 p.m.