Triple
T25222435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mon kingdoms |
E631998
|
entity |
| Predicate | receivedInfluenceFrom |
P87217
|
FINISHED |
| Object | Indian culture |
—
|
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: Indian culture | Statement: [Mon kingdoms, receivedInfluenceFrom, Indian culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivedInfluenceFrom Context triple: [Mon kingdoms, receivedInfluenceFrom, Indian culture]
-
A.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
B.
influencedPerson
Indicates that one entity has affected, shaped, or guided the thoughts, behavior, or development of another person.
-
C.
wereInfluencedBy
chosen
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
D.
influencesThrough
Indicates that one entity affects or alters another entity indirectly by means of an intermediate factor, channel, or mechanism.
-
E.
hasContactInfluenceFrom
Indicates that one entity’s state, behavior, or properties are directly affected or altered through physical or direct contact with another entity.
- 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_69e75a8e0f688190a7aebe9a4815e25b |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f47cc0578881909ed1e40c09fdc38d |
completed | May 1, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f4683472ec8190a483b3b8afe71720 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 1:03 p.m.