Triple
T31656281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New New York City |
E807865
|
entity |
| Predicate | hasLandmarkInFiction |
P116761
|
FINISHED |
| Object | Planet Express headquarters |
—
|
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: Planet Express headquarters | Statement: [New New York City, hasLandmarkInFiction, Planet Express headquarters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandmarkInFiction Context triple: [New New York City, hasLandmarkInFiction, Planet Express headquarters]
-
A.
hasFictionalLandmark
chosen
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
B.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
-
C.
hasPlaceInFiction
Indicates that a fictional work or element is associated with, set in, or takes place within a particular fictional location or setting.
-
D.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
E.
hasGroundsInFiction
Indicates that something is based on, justified by, or finds its origin within fictional works or narratives.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe5ec9028081909ae3d6fbe2f4cbbc |
completed | May 8, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69fe5e1d715881909fc516fafc707644 |
completed | May 8, 2026, 10:05 p.m. |
Created at: April 30, 2026, 10:55 p.m.