Triple
T34859056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tulip Olsen |
E1004812
|
entity |
| Predicate | hasTrainNumber |
P200477
|
FINISHED |
| Object | steadily decreasing glowing number on her hand |
—
|
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: steadily decreasing glowing number on her hand | Statement: [Tulip Olsen, hasTrainNumber, steadily decreasing glowing number on her hand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainNumber Context triple: [Tulip Olsen, hasTrainNumber, steadily decreasing glowing number on her hand]
-
A.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
-
B.
hasTrainIdentification
Indicates that an entity is associated with a specific train identification code or number used to uniquely identify that train.
-
C.
hasRailwayLineNumber
Indicates the specific identification number assigned to a railway line associated with an entity.
-
D.
railwayLineNumber
Indicates the identifying number assigned to a specific railway line within a rail network.
-
E.
trainTypeUsed
Indicates that a specific type or category of train is employed or operated in a given context or service.
- 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_69f76dbb678081909a247b9b5e1a73ac |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff8cecbf048190860b9f72b8753f5c |
completed | May 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69ff8c4c39dc8190b5bf35adc1bae7c6 |
completed | May 9, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69ff8cec1e4c8190b2d66b3e0f913bfd |
completed | May 9, 2026, 7:37 p.m. |
Created at: May 3, 2026, 4 p.m.