Triple
T34728176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alabama (fictional town) |
E1001127
|
entity |
| Predicate | hasLanguageInFiction |
P116831
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Alabama (fictional town), hasLanguageInFiction, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageInFiction Context triple: [Alabama (fictional town), hasLanguageInFiction, English]
-
A.
hasLanguageInUniverse
Indicates that a particular language exists or is used within a specified fictional or conceptual universe.
-
B.
hasPlaceInFiction
Indicates that a fictional work or element is associated with, set in, or takes place within a particular fictional location or setting.
-
C.
languageWithinFiction
chosen
Indicates that a language is used or exists within the context of a fictional work or fictional universe.
-
D.
hasRankInFiction
Indicates that a fictional character or entity holds a specific rank, title, or hierarchical position within a fictional context or universe.
-
E.
hasFeatureInFiction
Indicates that a fictional work includes or portrays a particular feature, trait, or characteristic.
- 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_69f76daeb6e48190a4c9a6b0edc80f72 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff519b65f081909902ba83b775ef85 |
completed | May 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69ff506fccdc8190bd93269589040aed |
completed | May 9, 2026, 3:19 p.m. |
Created at: May 3, 2026, 3:59 p.m.