Triple
T10805180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermopolis |
E254942
|
entity |
| Predicate | languageOfAncientName |
P52208
|
FINISHED |
| Object | Ancient Egyptian |
—
|
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: Ancient Egyptian | Statement: [Hermopolis, languageOfAncientName, Ancient Egyptian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfAncientName Context triple: [Hermopolis, languageOfAncientName, Ancient Egyptian]
-
A.
languageOfHistoricName
chosen
Indicates the language in which a historic or former name of an entity is expressed.
-
B.
ancientNameOf
Indicates that one entity is the historical or ancient name by which the other entity was formerly known.
-
C.
ancientLanguages
Indicates that the related entities are languages that originated in and were used during ancient historical periods.
-
D.
languageOfHistoricalRecord
Indicates the language in which a given historical record is written or recorded.
-
E.
traditionalLanguageName
Indicates the name traditionally used in a particular language to refer to the subject entity.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733726e2881908d4fe2cfe046ab43 |
completed | April 9, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69d6f3188f00819094ee8d65b187a333 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:18 p.m.