Triple
T26883672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heisenberg |
E676981
|
entity |
| Predicate | formerOccupationInRealIdentity |
P35945
|
FINISHED |
| Object | high school chemistry teacher |
—
|
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 school chemistry teacher | Statement: [Heisenberg, formerOccupationInRealIdentity, high school chemistry teacher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerOccupationInRealIdentity Context triple: [Heisenberg, formerOccupationInRealIdentity, high school chemistry teacher]
-
A.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
B.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
C.
occupationInDisguise
Indicates that an entity’s true occupation is being concealed or performed under a false or hidden identity.
-
D.
laterOccupationInFiction
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
E.
professionBeforeFBI
Indicates that a person held a particular profession or job prior to joining or working for the FBI.
- 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_69eee9bc0c90819085608c8bdc513a57 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69fce7671f108190bf3ebf54339068b5 |
completed | May 7, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69fce5b5a84c81908ac1b5b9f08d48d0 |
completed | May 7, 2026, 7:19 p.m. |
Created at: April 27, 2026, 5:41 a.m.