Triple
T234919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Armstrong, 1st Baron Armstrong |
E4487
|
entity |
| Predicate | educatedAs |
P8358
|
FINISHED |
| Object | lawyer |
—
|
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: lawyer | Statement: [William Armstrong, 1st Baron Armstrong, educatedAs, lawyer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educatedAs Context triple: [William Armstrong, 1st Baron Armstrong, educatedAs, lawyer]
-
A.
educatedIn
Indicates that an entity received education or formal training at a specified institution or place.
-
B.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
C.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
-
D.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
E.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25cc9ab2c81909af278a07f86aa1e |
completed | Feb. 28, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69a25b5dc640819092669575731c393f |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c2bda788190bcfc0bc94686f9e0 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.