Triple
T32738700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hijaz scale |
E837160
|
entity |
| Predicate | typicalDegreeCount |
P205145
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [Hijaz scale, typicalDegreeCount, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDegreeCount Context triple: [Hijaz scale, typicalDegreeCount, 7]
-
A.
typicalDegreeLength
Indicates the usual or standard duration of time required to complete a particular degree program.
-
B.
typicalDegree
Indicates the usual or characteristic level, intensity, or extent to which something holds or applies in a given context.
-
C.
typicalDegreeDistribution
Indicates that a degree distribution is characteristic or commonly observed for a given type of network or graph.
-
D.
hasNumberOfDegrees
Indicates the quantity of academic degrees that an entity possesses.
-
E.
typicalDegreeName
Indicates the standard or commonly used academic degree title associated with an educational program or qualification.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037cab06288190b093935f235ddff2 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:12 a.m.