Triple
T18867296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cynopolis |
E461471
|
entity |
| Predicate | religiousSpecialization |
P2154
|
FINISHED |
| Object | funerary cult of Anubis |
—
|
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: funerary cult of Anubis | Statement: [Cynopolis, religiousSpecialization, funerary cult of Anubis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousSpecialization Context triple: [Cynopolis, religiousSpecialization, funerary cult of Anubis]
-
A.
religiousSceneSpecialization
Indicates a relationship where a scene is specifically characterized or classified by a particular religious theme, context, or function.
-
B.
religiousElement
chosen
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
C.
religiousSpectrum
Indicates a relationship that places entities along a range or continuum of religious belief, practice, or affiliation.
-
D.
religiousTarget
Indicates that an action, policy, or behavior is directed at someone or something specifically because of their religion or religious affiliation.
-
E.
religiousTopicAddressed
Indicates that a subject deals with, discusses, or focuses on a religious theme, issue, or question.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c2a617548190be6044bf2c3de6d0 |
completed | April 20, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69e48d2166b88190add38de96cedc65c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:57 a.m.