Triple
T35836950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa S. (Lisa Selesner) |
E1035963
|
entity |
| Predicate | careerDevelopedIn |
P131085
|
FINISHED |
| Object | Hong Kong |
—
|
NE NERFINISHED |
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: Hong Kong | Statement: [Lisa S. (Lisa Selesner), careerDevelopedIn, Hong Kong]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerDevelopedIn Context triple: [Lisa S. (Lisa Selesner), careerDevelopedIn, Hong Kong]
-
A.
developsCareerIn
chosen
Indicates that an entity actively builds, advances, or pursues a professional path within a particular field, role, or organization.
-
B.
careerDevelopment
Indicates a relationship where one entity supports, influences, or engages in the growth, progression, or improvement of another entity’s professional path or skills.
-
C.
careerField
Indicates the professional domain or occupational area in which an entity works or specializes.
-
D.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
-
E.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
- 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_69f76e192a94819082db360cb91e6a8d |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.