Triple
T5608981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Lady and Gentleman in Black |
E147304
|
entity |
| Predicate | dateOfTheft |
P13733
|
FINISHED |
| Object | 1990-03-18 |
—
|
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: 1990-03-18 | Statement: [A Lady and Gentleman in Black, dateOfTheft, 1990-03-18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dateOfTheft Context triple: [A Lady and Gentleman in Black, dateOfTheft, 1990-03-18]
-
A.
stolenDuring
Indicates that one entity was stolen in the course of, or at the time of, another specified event or time period.
-
B.
theftStatus
Indicates the current state or condition of an entity with respect to being stolen, such as whether it has been reported, confirmed, or suspected as theft.
-
C.
stolenBy
Indicates that something has been taken unlawfully or without permission by a particular entity.
-
D.
locationOfInsigniaTheft
Indicates the place where the theft of an insignia occurred.
-
E.
dateOfLoss
chosen
Indicates the specific date on which a loss event (such as damage, theft, or other covered incident) occurred.
- 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020fe7ee0819088ced51afd9a4f93 |
completed | March 22, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69c01b1b3c98819080687d18ab10a914 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:39 p.m.