Triple
T26159502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dhul-Hulayfah |
E660068
|
entity |
| Predicate | miqatFor |
P160717
|
FINISHED |
| Object | pilgrims passing through Madinah |
—
|
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: pilgrims passing through Madinah | Statement: [Dhul-Hulayfah, miqatFor, pilgrims passing through Madinah]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: miqatFor Context triple: [Dhul-Hulayfah, miqatFor, pilgrims passing through Madinah]
-
A.
miqatFor
chosen
Indicates a designated boundary or starting point that serves as the proper location or time for initiating a specific religious or ritual act.
-
B.
hasSpecificMiqat
Indicates that a particular pilgrimage or pilgrim is associated with a specific miqat (designated boundary point for entering the state of ihram).
-
C.
qiblihFor
Indicates a directional or locational relationship specifying that something is oriented toward, aligned with, or designated for a particular qiblih (direction of prayer).
-
D.
موقعه_التقريبي
Indicates an approximate or rough location of an entity relative to a place or area.
-
E.
positionOnKaaba
Indicates that one entity occupies a specific location or placement on the surface or structure of the Kaaba.
- 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_69ee5bc5a9908190899d39ce95c6d215 |
completed | April 26, 2026, 6:39 p.m. |
| NER | Named-entity recognition | batch_69f6247480cc8190a887eedaeb94615c |
completed | May 2, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69f623a7539c8190b71797f583da9f63 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 26, 2026, 8:29 p.m.