Triple
T19601965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ned Poins |
E470500
|
entity |
| Predicate | relationshipToFalstaff |
P136437
|
FINISHED |
| Object | accomplice |
—
|
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: accomplice | Statement: [Ned Poins, relationshipToFalstaff, accomplice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFalstaff Context triple: [Ned Poins, relationshipToFalstaff, accomplice]
-
A.
roleInFalstaffArc
Indicates the specific function or contribution an entity has within the narrative or developmental arc associated with Falstaff.
-
B.
relationshipToSirTobyBelch
Indicates that one entity has a specified familial, social, or interpersonal connection to the character Sir Toby Belch.
-
C.
relationshipToFlorentinoAriza
Indicates the nature of the relationship an entity has with Florentino Ariza.
-
D.
relationshipToTheMiller'sTale
Indicates a relationship in which something is connected or relevant to "The Miller's Tale," such as being about it, derived from it, or otherwise associated with it.
-
E.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407f1fd88190aa82c4c96f755584 |
completed | April 20, 2026, 3:04 p.m. |
| PD | Predicate disambiguation | batch_69e514e166dc8190a0f147e0b4c8bbe7 |
completed | April 19, 2026, 5:46 p.m. |
| PDg | Predicate description generation | batch_69e5174b060c81908937ff9ff7fce611 |
completed | April 19, 2026, 5:56 p.m. |
Created at: April 10, 2026, 1:43 p.m.