Triple
T32485845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfred Hofstadter |
E830237
|
entity |
| Predicate | relationshipToBeverly |
P206293
|
FINISHED |
| Object | estranged husband |
—
|
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: estranged husband | Statement: [Alfred Hofstadter, relationshipToBeverly, estranged husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBeverly Context triple: [Alfred Hofstadter, relationshipToBeverly, estranged husband]
-
A.
relationshipToBeth
Indicates the specific type of relationship or connection that an entity has to Beth.
-
B.
relationshipToBenny
Indicates the specific type of personal or social relationship that an entity has with Benny.
-
C.
relationshipToRebecca
Indicates the specific type of relationship or connection an entity has to Rebecca.
-
D.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
-
E.
relationshipToBeaky
Indicates the specific type of relationship or connection that an entity has to Beaky.
- 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_69f34920aa4081908d8fb0277414b911 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
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, 12:58 a.m.