Triple
T382589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Darcy (Bridget Jones) |
E8711
|
entity |
| Predicate | professionDetail |
P2374
|
FINISHED |
| Object | specializes in human rights law |
—
|
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: specializes in human rights law | Statement: [Mark Darcy (Bridget Jones), professionDetail, specializes in human rights law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionDetail Context triple: [Mark Darcy (Bridget Jones), professionDetail, specializes in human rights law]
-
A.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
academicProfile
Indicates the relationship that captures an entity’s academic background, qualifications, and scholarly activities or achievements.
-
C.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
D.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
E.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec40ff8c81909306eb2dfe1512af |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e96602188190b0cbc167f55a9237 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.