Triple
T18537902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khaba |
E453013
|
entity |
| Predicate | historicalObscurity |
P132056
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Khaba, historicalObscurity, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalObscurity Context triple: [Khaba, historicalObscurity, high]
-
A.
historicFor
Indicates that something holds historical significance or importance specifically in relation to another entity.
-
B.
historicalNotability
Indicates that an entity is recognized as having significant importance, influence, or prominence in history.
-
C.
historicallyIn
Indicates that one entity existed, occurred, or was situated within the historical context, period, or jurisdiction associated with another entity.
-
D.
historicallySpoke
Indicates that an entity used a particular language as a spoken language during some period in the past.
-
E.
historicallyConsidered
Indicates that one entity has been regarded or classified in a particular way relative to another entity during a past historical period.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534030bd88190b25b95305a12a0c1 |
completed | April 19, 2026, 7:58 p.m. |
| PD | Predicate disambiguation | batch_69e469e0025c81908f16ed4f922674af |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:37 a.m.