Triple
T4758928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hierophant |
E105654
|
entity |
| Predicate | familyAssociation |
P8517
|
FINISHED |
| Object | Kerykes |
E112591
|
NE 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: Kerykes | Statement: [Hierophant, familyAssociation, Kerykes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kerykes Context triple: [Hierophant, familyAssociation, Kerykes]
-
A.
Kerykes
chosen
Kerykes was an important Athenian priestly family closely associated with the sacred rites and hereditary offices of the Eleusinian Mysteries.
-
B.
Cleon
Cleon was an influential Athenian statesman and general during the Peloponnesian War, known for his aggressive policies and prominent role in Athenian politics.
-
C.
Xocrates
Xocrates is a character in Xenophon’s philosophical dialogue "Symposium," depicted as one of the participants in the work’s discussions on love and virtue.
-
D.
Cirón
Cirón is a river in southwestern France known for flowing through the Sauternes wine region, where its cool misty microclimate helps produce the area’s famous sweet wines.
-
E.
Dilios
Dilios is the Spartan soldier and narrator in the film "300," known for recounting King Leonidas's stand at Thermopylae.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd43f14cac819081c7c69803648211 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c3f283c8190b2d18ad5159a35ed |
completed | March 20, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be3a77eb848190877eb5e15c7e6b0c |
completed | March 21, 2026, 6:28 a.m. |
Created at: March 20, 2026, 1:20 p.m.