Triple
T23882548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Date for Gomer |
E600240
|
entity |
| Predicate | hasFictionalGasStation |
P153928
|
FINISHED |
| Object | Wally's Filling Station |
—
|
NE NERFINISHED |
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: Wally's Filling Station | Statement: [A Date for Gomer, hasFictionalGasStation, Wally's Filling Station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalGasStation Context triple: [A Date for Gomer, hasFictionalGasStation, Wally's Filling Station]
-
A.
hasFictionalPub
Indicates that an entity features or includes a fictional pub as part of its content, setting, or structure.
-
B.
hasFictionalChipShop
Indicates that an entity is associated with, or features, a fictional chip shop (e.g., as a setting, location, or element in a narrative).
-
C.
hasFictionalDiner
Indicates that one entity features or includes a fictional diner associated with another entity.
-
D.
hasFictionalTubeStation
Indicates that an entity features or is associated with a tube (subway) station that exists only in fiction rather than in reality.
-
E.
hasFictionalRadioStation
Indicates that an entity includes, features, or is associated with a fictional radio station within its context or content.
- 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_69e295318e148190b9979d8fc02e168f |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ccfaef348190b4820b6f3648c60c |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 8:24 p.m.