Triple
T28540154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telepinu |
E722267
|
entity |
| Predicate | genreOfEdict |
P168625
|
FINISHED |
| Object | historical-legal text |
—
|
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: historical-legal text | Statement: [Telepinu, genreOfEdict, historical-legal text]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genreOfEdict Context triple: [Telepinu, genreOfEdict, historical-legal text]
-
A.
genreOfJudgment
Indicates the specific legal category or type under which a particular judgment is classified.
-
B.
genreOfRecognition
Indicates the specific genre or category in which an entity (such as a work or person) is formally recognized, honored, or awarded.
-
C.
numberOfEdicts
Indicates the total count of edicts associated with or issued by a given entity.
-
D.
ecclesiasticalGenre
Indicates that one entity is classified as having a particular ecclesiastical (church-related or liturgical) genre in relation to another entity.
-
E.
scriptUsedInEdicts
Indicates that a particular writing system or script is employed in the creation or inscription of official edicts or decrees.
- 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_69f01a5e42348190b1ffbca26e739c84 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f67595fa7c8190b6e9f7a8c700dd97 |
completed | May 2, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
| PDg | Predicate description generation | batch_69f674df80b08190adb7f7531083bbb1 |
completed | May 2, 2026, 10:04 p.m. |
Created at: April 28, 2026, 3:34 a.m.