Triple
T6590234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charmian Kittredge London |
E159334
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Charmian
Charmian was an American writer and adventurer best known as the second wife and literary partner of novelist Jack London.
|
E599566
|
NE FINISHED |
How this triple was built (4 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: [Charmian Kittredge London, givenName, Charmian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charmian Context triple: [Charmian Kittredge London, givenName, Charmian]
-
A.
Charmian
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.
Thisbe
Thisbe was an ancient town in Boeotia, Greece, known from classical sources and mythology and situated near the Corinthian Gulf.
-
D.
Daphne
Daphne is a nymph from Greek mythology best known for being pursued by Apollo and transformed into a laurel tree to escape him.
-
E.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Charmian Triple: [Charmian Kittredge London, givenName, Charmian]
Generated description
Charmian was an American writer and adventurer best known as the second wife and literary partner of novelist Jack London.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charmian Target entity description: Charmian was an American writer and adventurer best known as the second wife and literary partner of novelist Jack London.
-
A.
Charmian
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.
Thisbe
Thisbe was an ancient town in Boeotia, Greece, known from classical sources and mythology and situated near the Corinthian Gulf.
-
D.
Daphne
Daphne is a nymph from Greek mythology best known for being pursued by Apollo and transformed into a laurel tree to escape him.
-
E.
Daphne
Daphne is an HTTP, HTTP/2, and WebSocket server for ASGI applications, commonly used to serve Django and other Python async web frameworks.
- F. None of above. chosen
Provenance (5 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_69c688366ce8819083f8883983c0df92 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6aecc969c81909a6e15ebe8dd3f94 |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbb568508190b9da3475d1620ed5 |
completed | March 27, 2026, 6:25 p.m. |
| NEDg | Description generation | batch_69c6cd08a9c88190a481d4d3f8e680bf |
completed | March 27, 2026, 6:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cdc859cc8190bbae2efc39409021 |
completed | March 27, 2026, 6:34 p.m. |
Created at: March 27, 2026, 1:55 p.m.