Triple
T21502419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greg Behrendt |
E530508
|
entity |
| Predicate | basedBookOn |
P124205
|
FINISHED |
| Object | advice given on Sex and the City |
—
|
LITERAL 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: advice given on Sex and the City | Statement: [Greg Behrendt, basedBookOn, advice given on Sex and the City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedBookOn Context triple: [Greg Behrendt, basedBookOn, advice given on Sex and the City]
-
A.
basedOnInFiction
Indicates that a fictional work, character, or element is derived from, inspired by, or modeled after another real or fictional source.
-
B.
bookTieIn
chosen
Indicates that one creative work is directly related to another as a tie-in, typically produced to promote, expand, or accompany the original work (such as a book based on a film, game, or TV series).
-
C.
basedOnAuthor
Indicates that one entity is derived from, inspired by, or otherwise created on the basis of the work or contributions of a particular author.
-
D.
basedOnBy
Indicates that one entity is derived from, justified by, or constructed using another entity as its source, foundation, or reference.
-
E.
bookAdaptedInto
Indicates that a book has been turned into another work, typically in a different medium such as a film, TV series, or play.
- F. None of above.
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_69e0c45bd15481909fba5910765cdda2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea5d209881908754eb07a47e478a |
completed | April 23, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69e631f6e68081908f5ee4ce7413803e |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:24 p.m.