Triple
T37982432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patti Levin |
E947589
|
entity |
| Predicate | roleWithMegAbbott |
P189952
|
FINISHED |
| Object | mentors Meg Abbott in the Guilty Remnant |
—
|
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: mentors Meg Abbott in the Guilty Remnant | Statement: [Patti Levin, roleWithMegAbbott, mentors Meg Abbott in the Guilty Remnant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleWithMegAbbott Context triple: [Patti Levin, roleWithMegAbbott, mentors Meg Abbott in the Guilty Remnant]
-
A.
roleOfJennifer Morris
Indicates that Jennifer Morris holds or performs a particular role, position, or function in relation to another entity or context.
-
B.
roleOfCathy Hughes
Indicates the specific position, function, or capacity that Cathy Hughes holds or performs in relation to another entity or context.
-
C.
roleInAtomicBlonde
Indicates that an entity has a specific role or participation in the film "Atomic Blonde."
-
D.
writtenByWoman
Indicates that the work or content in question was authored or created by a female individual.
-
E.
roleInMadMen
Indicates that one entity has a specific role or character in the television series "Mad Men" in relation to another entity.
- F. None of above. chosen
Provenance (4 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_69f76ef8a1d08190a741bbbc5970e3b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69fc4747b06c8190a3ea5331f02eedad |
completed | May 7, 2026, 8:03 a.m. |
Created at: May 3, 2026, 4:20 p.m.