Triple
T22002084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosa Coldfield |
E543350
|
entity |
| Predicate | relationshipToQuentinCompson |
P110481
|
FINISHED |
| Object | older towns-woman and narrator |
—
|
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: older towns-woman and narrator | Statement: [Rosa Coldfield, relationshipToQuentinCompson, older towns-woman and narrator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToQuentinCompson Context triple: [Rosa Coldfield, relationshipToQuentinCompson, older towns-woman and narrator]
-
A.
relationshipToQuentinCompsonIII
chosen
Indicates the specific familial or social relationship that an entity has to Quentin Compson III.
-
B.
relationshipToJasonCompsonIV
Indicates the specific familial or interpersonal relationship that an entity has to Jason Compson IV.
-
C.
relationshipToCarolineCompson
Indicates the specific familial or social relationship that an entity has to Caroline Compson.
-
D.
relationshipWithNinaSimone
Indicates having some form of personal, professional, or significant social connection to Nina Simone.
-
E.
relationshipToQuentinJacobsen
Indicates the specific type of relationship or connection an entity has to Quentin Jacobsen.
- F. None of above.
Provenance (3 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1276bf2a48190910d9c27f1c5e74f |
completed | April 28, 2026, 9:32 p.m. |
| PD | Predicate disambiguation | batch_69e6f62dc9d88190ae387f145f9528de |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:20 p.m.