Triple
T32039004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khun-Anup the peasant |
E818170
|
entity |
| Predicate | speechCount |
P82677
|
FINISHED |
| Object | multiple petitions to authority |
—
|
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: multiple petitions to authority | Statement: [Khun-Anup the peasant, speechCount, multiple petitions to authority]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: speechCount Context triple: [Khun-Anup the peasant, speechCount, multiple petitions to authority]
-
A.
numberOfSpeeches
chosen
Indicates the total count of speeches associated with a given entity or event.
-
B.
spokeWords
Indicates that one entity verbally expressed specific words to another entity or audience.
-
C.
spokenWord
Indicates that one entity has uttered or articulated a specific word or phrase in spoken form.
-
D.
speakerNumber
Indicates the number of distinct speakers involved in a given speech, dialogue, or conversational instance.
-
E.
simCount
Indicates the number of times two entities or items are considered similar according to a defined similarity measure.
- 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_69f348fbc8148190b3c0f95d4772b153 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b49e063c819080287830c83f4207 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, 12:19 a.m.