Triple
T15214704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colegrove v. Green |
E363606
|
entity |
| Predicate | hasLaterTreatment |
P58195
|
FINISHED |
| Object | partially overruled by Baker v. Carr |
—
|
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: partially overruled by Baker v. Carr | Statement: [Colegrove v. Green, hasLaterTreatment, partially overruled by Baker v. Carr]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLaterTreatment Context triple: [Colegrove v. Green, hasLaterTreatment, partially overruled by Baker v. Carr]
-
A.
hasSubsequentTreatment
chosen
Indicates that one treatment occurs after and in continuation of another treatment in a temporal sequence.
-
B.
subsequentTreatment
Indicates that one treatment occurs after and in response to a prior treatment or medical event.
-
C.
hasReceivedTreatmentFor
Indicates that an entity has undergone or been given a treatment in relation to a specified condition, issue, or problem.
-
D.
hasLaterInvestigation
Indicates that one investigation occurs after and follows from another in time.
-
E.
hasTreatmentConsideration
Indicates that a particular factor, condition, or option should be taken into account when planning, selecting, or managing a treatment.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:11 a.m.