Triple
T35399932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meg Manning |
E1023196
|
entity |
| Predicate | oneOfFewFriendlyClassmatesOf |
P183284
|
FINISHED |
| Object | Veronica Mars |
E81053
|
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: Veronica Mars | Statement: [Meg Manning, oneOfFewFriendlyClassmatesOf, Veronica Mars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oneOfFewFriendlyClassmatesOf Context triple: [Meg Manning, oneOfFewFriendlyClassmatesOf, Veronica Mars]
-
A.
oneOfFew
Indicates that the subject is one member of a small, limited set of entities that share a specified property or role.
-
B.
childhoodFriend
Indicates that two people were close friends during their childhood period.
-
C.
hasClassmate
Indicates that two people attend the same class or course, making them classmates.
-
D.
oneOfFewIn
Indicates that an entity is one among a small, limited number of entities that are in or belong to a specified set, group, or context.
-
E.
highSchoolTeammateOf
Indicates that two people were teammates on the same high school sports team or organized high school activity team.
- 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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a384024a1a881909eaea938f0346a53 |
completed | June 21, 2026, 7:48 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
| PDg | Predicate description generation | batch_69f79da8d8848190ab5ab1bdad95d58c |
completed | May 3, 2026, 7:10 p.m. |
Created at: May 3, 2026, 4:03 p.m.