Triple
T12237062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert H. McKinney School of Law |
E291622
|
entity |
| Predicate | hasStrengthIn |
P40124
|
FINISHED |
| Object | government relations |
—
|
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: government relations | Statement: [Robert H. McKinney School of Law, hasStrengthIn, government relations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrengthIn Context triple: [Robert H. McKinney School of Law, hasStrengthIn, government relations]
-
A.
hasNotableStrengthIn
chosen
Indicates that an entity possesses a particularly high level of ability, effectiveness, or advantage in a specific area or domain.
-
B.
hasForceStrength
Indicates that one entity possesses a certain level or degree of physical or exerted force strength in relation to another entity or context.
-
C.
hasStrengthDescriptor
Indicates that an entity is associated with a qualitative description of its strength or intensity.
-
D.
hasStrongback
Indicates that one entity possesses or is equipped with a structural support element referred to as a strongback, typically used to reinforce or stabilize it.
-
E.
strongerAt
Indicates that one entity has greater strength, power, or effectiveness than another in a specified context, condition, or domain.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.