Triple
T12943158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Doctor (A Streetcar Named Desire) |
E309687
|
entity |
| Predicate | relationshipToBlancheDuBois |
P107614
|
FINISHED |
| Object | takes Blanche into his care |
—
|
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: takes Blanche into his care | Statement: [The Doctor (A Streetcar Named Desire), relationshipToBlancheDuBois, takes Blanche into his care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBlancheDuBois Context triple: [The Doctor (A Streetcar Named Desire), relationshipToBlancheDuBois, takes Blanche into his care]
-
A.
relationshipToMissWatson
Indicates the type or nature of a person's relational connection to Miss Watson (e.g., familial, social, or other defined relationship).
-
B.
relationshipToHuck
Indicates the specific type of personal or social relationship that one entity has with Huck.
-
C.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
-
D.
relationshipToHuckFinn
Indicates the specific type of personal or social relationship an entity has to Huck Finn.
-
E.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:43 p.m.