Triple
T32895168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penny Fleck |
E841449
|
entity |
| Predicate | pastEmployment |
P1910
|
FINISHED |
| Object | worked for Thomas Wayne at Wayne Manor |
—
|
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: worked for Thomas Wayne at Wayne Manor | Statement: [Penny Fleck, pastEmployment, worked for Thomas Wayne at Wayne Manor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pastEmployment Context triple: [Penny Fleck, pastEmployment, worked for Thomas Wayne at Wayne Manor]
-
A.
hasPastOccupation
Indicates that an entity previously held a particular job, role, or occupation in the past.
-
B.
earlierOccupation
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
C.
formerEmployer
chosen
Indicates that one entity previously employed the other but no longer does so.
-
D.
peakEmployment
Indicates that an entity has reached its highest level of employment or workforce size during a specified period.
-
E.
employedAfter
Indicates that one entity became employed by another entity at a point in time later than a referenced employment event or period.
- 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_69f34945ae408190b72d8118c83beb77 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:18 a.m.