Triple
T15207811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Room 25 |
E363434
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Regal
Regal is a character featured in the puzzle-adventure video game "Room 25."
|
E1143104
|
NE FINISHED |
How this triple was built (4 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: Regal | Statement: [Room 25, hasPart, Regal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Regal Context triple: [Room 25, hasPart, Regal]
-
A.
Regal
Regal is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
B.
Noble
Noble is a surname of English origin historically associated with social rank and often borne by families of distinction.
-
C.
Noble
Noble is an unincorporated community and residential area within Abington Township in Montgomery County, Pennsylvania.
-
D.
Noble
Noble is a small city in Cleveland County, Oklahoma, known for its close-knit community and proximity to the Oklahoma City metropolitan area.
-
E.
Royal
Royal is a French surname most prominently associated with politician Ségolène Royal, a leading figure in contemporary French public life.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Regal Triple: [Room 25, hasPart, Regal]
Generated description
Regal is a character featured in the puzzle-adventure video game "Room 25."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Regal Target entity description: Regal is a character featured in the puzzle-adventure video game "Room 25."
-
A.
Regal
Regal is a major American movie theater chain known for operating multiplex cinemas across the United States.
-
B.
Noble
Noble is a surname of English origin historically associated with social rank and often borne by families of distinction.
-
C.
Noble
Noble is a small city in Cleveland County, Oklahoma, known for its close-knit community and proximity to the Oklahoma City metropolitan area.
-
D.
Noble
Noble is an unincorporated community and residential area within Abington Township in Montgomery County, Pennsylvania.
-
E.
Royal
Royal is a French surname most prominently associated with politician Ségolène Royal, a leading figure in contemporary French public life.
- F. None of above. chosen
Provenance (5 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b8e2788190bd1831762e4181ae |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed33dbda08190a10ba81082d0d183 |
completed | May 9, 2026, 6:25 a.m. |
| NEDg | Description generation | batch_69fed47c88d08190a4396b955c9bb388 |
completed | May 9, 2026, 6:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed50956408190b1426d578803974e |
completed | May 9, 2026, 6:32 a.m. |
Created at: April 10, 2026, 3:11 a.m.