Triple
T33117943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Becca Butcher |
E847507
|
entity |
| Predicate | relationshipToBillyButcher |
P206356
|
FINISHED |
| Object | primary emotional anchor |
—
|
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: primary emotional anchor | Statement: [Becca Butcher, relationshipToBillyButcher, primary emotional anchor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBillyButcher Context triple: [Becca Butcher, relationshipToBillyButcher, primary emotional anchor]
-
A.
relationshipToTheDude
Indicates the specific type of personal or social relationship that one entity has to the individual referred to as "the Dude."
-
B.
relationshipToJoeCinque
Indicates a specified type of relationship or connection that an entity has to Joe Cinque.
-
C.
relationshipToPonyboy
Indicates the specific way an entity is connected or related to Ponyboy.
-
D.
relationshipToWalterBurns
Indicates the specific nature of the relationship an entity has with Walter Burns, such as familial, professional, or social connection.
-
E.
relationshipToMrsLovett
Indicates the specific personal or social relationship that an entity has to Mrs. Lovett.
- 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_69f3495751a081909850af5843da40dc |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:27 a.m.