Triple
T21611818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASA Award |
E533329
|
entity |
| Predicate | hasFictionalPrestigeLevel |
P118240
|
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: [ASA Award, hasFictionalPrestigeLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalPrestigeLevel Context triple: [ASA Award, hasFictionalPrestigeLevel, high]
-
A.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
B.
hasFictionalHierarchy
chosen
Indicates that one entity occupies a specific level, rank, or position within a fictional or imagined hierarchical structure defined by another entity.
-
C.
hasFictionalPromotion
Indicates that an entity has received a rank, title, or status advancement that occurs only within a fictional or narrative context, not in real life.
-
D.
hasFictionalSpecialization
Indicates that an entity’s area of focus, expertise, or role is within a fictional or imaginative domain rather than a real-world specialization.
-
E.
hasFictionalAlias
Indicates that an entity is known by an alternative name or identity within a fictional context.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3ba79424819094e9ee93c4bbcc0b |
completed | April 27, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e69665fe8c8190af7e38785db188b2 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:33 p.m.