Triple
T3505506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Lieutenant |
E74064
|
entity |
| Predicate | hasSupportBody |
P48595
|
FINISHED |
| Object | lieutenancy office |
—
|
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: lieutenancy office | Statement: [Lord Lieutenant, hasSupportBody, lieutenancy office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSupportBody Context triple: [Lord Lieutenant, hasSupportBody, lieutenancy office]
-
A.
supportsBody
Indicates that one entity physically or structurally holds up, bears the weight of, or provides foundational stability for another entity’s body.
-
B.
hasBodyOf
Indicates that one entity possesses, contains, or is composed of the physical body or main substance of another entity.
-
C.
hasWorkingBody
Indicates that an entity possesses a functional, operational physical body.
-
D.
supportsBodyType
Indicates that one entity is compatible with, designed for, or can accommodate a specified body type.
-
E.
hasPolicyBody
Indicates that an entity is associated with, or defined by, a specific policy document or policy text.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf38e988190998d722b95830411 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0cd8b0819099da300af09880da |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaef1037c819082c7af949ec85360 |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:18 p.m.