Triple
T21422637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HBG |
E528473
|
entity |
| Predicate | majorAreaOfWork |
P106920
|
FINISHED |
| Object | roads and highways |
—
|
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: roads and highways | Statement: [HBG, majorAreaOfWork, roads and highways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorAreaOfWork Context triple: [HBG, majorAreaOfWork, roads and highways]
-
A.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
B.
workedPrimarilyOn
chosen
Indicates that an entity devoted the majority of its work, effort, or activity to a particular project, field, or subject.
-
C.
majorField
Indicates the primary academic discipline or field of study in which an entity (typically a person or program) specializes.
-
D.
primaryWork
Indicates that one work is the main or most significant work associated with a given entity, as opposed to other secondary or related works.
-
E.
competenceArea
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee62d392e08190b7f378005afc9026 |
completed | April 26, 2026, 7:09 p.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:48 p.m.