Triple
T19062674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pala |
E466572
|
entity |
| Predicate | influencesFromAuthor |
P134940
|
FINISHED |
| Object | Aldous Huxley’s interest in Vedanta |
—
|
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: Aldous Huxley’s interest in Vedanta | Statement: [Pala, influencesFromAuthor, Aldous Huxley’s interest in Vedanta]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencesFromAuthor Context triple: [Pala, influencesFromAuthor, Aldous Huxley’s interest in Vedanta]
-
A.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
B.
hasInfluentialAuthor
Indicates that an entity has an author whose work has had significant impact or influence in a relevant domain.
-
C.
influencedWork
Indicates that one work has had a significant impact on the creation, style, content, or development of another work.
-
D.
influencedPerson
Indicates that one entity has affected, shaped, or guided the thoughts, behavior, or development of another person.
-
E.
literaryInfluence
Indicates that one entity has had a significant impact on the style, themes, or development of another entity’s literary work.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e195ac048190a57b57e585d022d4 |
completed | April 20, 2026, 8:19 a.m. |
| PD | Predicate disambiguation | batch_69e4b99633c8819097988608c278ecf8 |
completed | April 19, 2026, 11:16 a.m. |
| PDg | Predicate description generation | batch_69e4c0fc3c4c8190abbfe06e1bd3325c |
completed | April 19, 2026, 11:48 a.m. |
Created at: April 10, 2026, 12:03 p.m.