Triple
T36127326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hubermann house on Himmel Street |
E1044917
|
entity |
| Predicate | locatedInTownFictional |
P126434
|
FINISHED |
| Object | Molching |
E322816
|
NE 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: Molching | Statement: [Hubermann house on Himmel Street, locatedInTownFictional, Molching]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInTownFictional Context triple: [Hubermann house on Himmel Street, locatedInTownFictional, Molching]
-
A.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
B.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
C.
basedInFictionalWorkLocation
chosen
Indicates that an entity’s location or setting is situated within a fictional place as depicted in a specific creative work.
-
D.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
E.
residesInFictionalLocation
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
- F. None of above.
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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a397d6a0ee48190a3b3f6023de7f8f2 |
completed | June 22, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:08 p.m.