Triple
T9328653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Tower |
E224459
|
entity |
| Predicate | hasFictionalEventType |
P87547
|
FINISHED |
| Object | high-rise fire disaster |
—
|
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-rise fire disaster | Statement: [The Tower, hasFictionalEventType, high-rise fire disaster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalEventType Context triple: [The Tower, hasFictionalEventType, high-rise fire disaster]
-
A.
hasFictionalFunction
Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
-
B.
hasFictionalProperty
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
-
C.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
D.
hasFictionalProductionType
Indicates that an entity is associated with a specific type or category of fictional production (such as a genre, format, or style).
-
E.
hasFictionalIssue
Indicates that one entity possesses, is associated with, or is characterized by a particular fictional problem, flaw, or complication.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37ab90ac8190b5c73f08dd091731 |
completed | April 1, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc94b796788190816b71b1e9996288 |
completed | April 1, 2026, 3:44 a.m. |
Created at: March 30, 2026, 7:39 p.m.