Triple
T4170694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Time rotor |
E84553
|
entity |
| Predicate | designVariesBy |
P51402
|
FINISHED |
| Object | Doctor’s TARDIS console room redesigns |
—
|
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: Doctor’s TARDIS console room redesigns | Statement: [Time rotor, designVariesBy, Doctor’s TARDIS console room redesigns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designVariesBy Context triple: [Time rotor, designVariesBy, Doctor’s TARDIS console room redesigns]
-
A.
structureVariesBy
chosen
Indicates that the structure or configuration of one entity changes depending on or is different for another specified factor or context.
-
B.
lengthVariesBy
Indicates that the length of one entity changes or differs depending on another specified factor or condition.
-
C.
termVariesBy
Indicates that the value or meaning of a term changes depending on a specified factor, such as context, dimension, or condition.
-
D.
usageVariesBy
Indicates that the way something is used differs depending on a specified factor, such as context, user, location, or conditions.
-
E.
viewVariesAmong
Indicates that the way something is viewed, perceived, or interpreted differs across multiple entities or contexts.
- 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_69aed932cab48190b80ffe35f7029ae1 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af02c87cc88190a9ec3712db18a8a7 |
completed | March 9, 2026, 5:26 p.m. |
| PD | Predicate disambiguation | batch_69af018fb0948190a9701b2e8e5d9bac |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:45 p.m.