Triple
T20713323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Universal Orlando’s Horror Make-Up Show |
E509100
|
entity |
| Predicate | queueAreaFeature |
P141198
|
FINISHED |
| Object | displays of horror movie props and posters |
—
|
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: displays of horror movie props and posters | Statement: [Universal Orlando’s Horror Make-Up Show, queueAreaFeature, displays of horror movie props and posters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: queueAreaFeature Context triple: [Universal Orlando’s Horror Make-Up Show, queueAreaFeature, displays of horror movie props and posters]
-
A.
queueAreaTheme
Indicates the thematic style or concept applied to the area where people wait in line for an attraction or service.
-
B.
queueLocation
Indicates the place or position where an entity is arranged to wait in a queue or line.
-
C.
queueEntranceName
Indicates the name assigned to the entrance of a queue.
-
D.
queueType
Indicates the classification or category of a queue that specifies how items in it are organized, prioritized, or processed.
-
E.
queueTypical
Indicates that an entity is in or follows a standard or commonly expected queueing order or behavior relative to others.
- 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_69e0b4c40ad88190b81f77695366d328 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d001788190a524a17347f9de67 |
completed | April 21, 2026, 12:16 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:15 p.m.