Triple
T12616883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Zane |
E301276
|
entity |
| Predicate | hasProfessionSkill |
P96902
|
FINISHED |
| Object | legal research |
—
|
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: legal research | Statement: [Rachel Zane, hasProfessionSkill, legal research]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionSkill Context triple: [Rachel Zane, hasProfessionSkill, legal research]
-
A.
skilledIn
Indicates that an entity possesses ability, expertise, or proficiency in performing or using another entity (such as a task, tool, or domain).
-
B.
indicatesSkill
chosen
Indicates a relationship where one entity possesses, demonstrates, or is associated with a particular skill represented by another entity.
-
C.
hasEponymousSkill
Indicates that an entity possesses a skill that is named after a particular person or entity.
-
D.
hasCompetence
Indicates that an entity possesses the ability, skill, or qualification to perform a specific task or function effectively.
-
E.
hasInterpreterProfession
Indicates that an entity works in the professional role or occupation of an interpreter.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b195108190ac25bd95e644ace4 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:13 p.m.