Triple
T26138300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | معبد بتاح في منف |
E659441
|
entity |
| Predicate | كان محاطًا بـ |
P7850
|
FINISHED |
| Object | منشآت دينية وإدارية أخرى في منف |
—
|
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: منشآت دينية وإدارية أخرى في منف | Statement: [معبد بتاح في منف, كان محاطًا بـ, منشآت دينية وإدارية أخرى في منف]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: كان محاطًا بـ Context triple: [معبد بتاح في منف, كان محاطًا بـ, منشآت دينية وإدارية أخرى في منف]
-
A.
surrounds
chosen
Indicates that one entity is located all around another entity, enclosing or encircling it on multiple sides or completely.
-
B.
previouslySurroundedBy
Indicates that an entity was, at some earlier time, completely encircled or enclosed by another entity or group of entities.
-
C.
surroundedOnThreeSidesBy
Indicates that one entity is positioned so that three of its sides are directly bordered or enclosed by another entity.
-
D.
surroundedButDidNotInclude
Indicates that one entity completely encircled another in space or extent, while explicitly excluding that inner entity from its own area or membership.
-
E.
surroundedByStreet
Indicates that an entity is encircled or enclosed on all sides by one or more streets.
- 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_69ee5bc3c20c8190bf2cf272f4170e95 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60c698ae48190871cd445422bad91 |
completed | May 2, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69f60b874cc88190a487230abb69efea |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 26, 2026, 8:18 p.m.