Triple
T15094926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akbarnama |
E360513
|
entity |
| Predicate | thirdVolumeContent |
P117288
|
FINISHED |
| Object | administration, institutions, and regulations of Akbar’s empire |
—
|
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: administration, institutions, and regulations of Akbar’s empire | Statement: [Akbarnama, thirdVolumeContent, administration, institutions, and regulations of Akbar’s empire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thirdVolumeContent Context triple: [Akbarnama, thirdVolumeContent, administration, institutions, and regulations of Akbar’s empire]
-
A.
thirdPartTitle
Indicates that an entity holds the title or designation of the third part in a sequence, series, or multipart structure.
-
B.
thirdElement
Indicates that one entity is the third element in an ordered sequence or tuple associated with another entity.
-
C.
thirdEpisode
Indicates that one entity is the third episode in sequence within a series or season relative to another entity.
-
D.
thirdTier
Indicates that an entity occupies a third level or rank within a hierarchical structure or classification.
-
E.
thirdWord
Indicates that one entity is the third word in sequence within another entity (such as a text or phrase).
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0054571a48190a57055c0d6e90f82 |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:04 a.m.