Triple
T36860690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nerikare |
E910925
|
entity |
| Predicate | knownMonuments |
P186596
|
FINISHED |
| Object | none securely identified |
—
|
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: none securely identified | Statement: [Nerikare, knownMonuments, none securely identified]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownMonuments Context triple: [Nerikare, knownMonuments, none securely identified]
-
A.
otherMonuments
Indicates that there exists a relationship between an entity and additional monuments that are associated with or related to it in some relevant way.
-
B.
monumentsLocation
Indicates the geographical place where a monument is situated or located.
-
C.
hasNumberOfMonuments
Indicates the specific count of monuments associated with or present in a given entity.
-
D.
builtMonument
Indicates that one entity constructed or created a monument in honor of, or related to, another entity.
-
E.
monumentSubject
Indicates that the subject serves as the monument or commemorative structure associated with another entity.
- 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fe1a1ca4819084c196f0041f0be2 |
completed | May 5, 2026, 2:26 p.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
| PDg | Predicate description generation | batch_69f9fd66eed48190bdc26a8def328c2d |
completed | May 5, 2026, 2:23 p.m. |
Created at: May 3, 2026, 4:13 p.m.