Triple
T20416767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detection Club |
E500732
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Liza Cody
Liza Cody is a British crime fiction author best known for her pioneering female private-eye novels, including the Anna Lee series.
|
E1434361
|
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: Liza Cody | Statement: [Detection Club, hasMember, Liza Cody]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liza Cody Context triple: [Detection Club, hasMember, Liza Cody]
-
A.
Liza Elliott
Liza Elliott is the conflicted, high-powered fashion magazine editor whose psychoanalytic journey drives the plot of the musical "Lady in the Dark."
-
B.
Liza Miller
Liza Miller is the 40-year-old divorced mother who pretends to be in her twenties to restart her publishing career in the TV series "Younger."
-
C.
Liza Marshall
Liza Marshall is a British film and television producer known for her work on projects such as "Before I Go to Sleep" and "Temple."
-
D.
Liza Todd
Liza Todd is an American sculptor and the daughter of actress Elizabeth Taylor and producer Mike Todd.
-
E.
Liza Goddard
Liza Goddard is a British actress best known for her extensive work in television drama and comedy from the 1960s onward.
- 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: Liza Cody Triple: [Detection Club, hasMember, Liza Cody]
Generated description
Liza Cody is a British crime fiction author best known for her pioneering female private-eye novels, including the Anna Lee series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liza Cody Target entity description: Liza Cody is a British crime fiction author best known for her pioneering female private-eye novels, including the Anna Lee series.
-
A.
Liza Elliott
Liza Elliott is the conflicted, high-powered fashion magazine editor whose psychoanalytic journey drives the plot of the musical "Lady in the Dark."
-
B.
Liza Miller
Liza Miller is the 40-year-old divorced mother who pretends to be in her twenties to restart her publishing career in the TV series "Younger."
-
C.
Liza Marshall
Liza Marshall is a British film and television producer known for her work on projects such as "Before I Go to Sleep" and "Temple."
-
D.
Liza Todd
Liza Todd is an American sculptor and the daughter of actress Elizabeth Taylor and producer Mike Todd.
-
E.
Liza Goddard
Liza Goddard is a British actress best known for her extensive work in television drama and comedy from the 1960s onward.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a4437448190b07b6e6e3de5830f |
completed | April 20, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0893a1996481909cffd0558181cf40 |
completed | May 16, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_6a0894dc926081908c1c6d0b1c8192c7 |
completed | May 16, 2026, 4:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08954be2688190a51911d6922a6413 |
completed | May 16, 2026, 4:03 p.m. |
Created at: April 16, 2026, 11:30 a.m.