Triple
T12447675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hungo Pavi |
E297443
|
entity |
| Predicate | hasApproximateRoomCount |
P51690
|
FINISHED |
| Object | more than 100 rooms |
—
|
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: more than 100 rooms | Statement: [Hungo Pavi, hasApproximateRoomCount, more than 100 rooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateRoomCount Context triple: [Hungo Pavi, hasApproximateRoomCount, more than 100 rooms]
-
A.
approximateNumberOfRooms
chosen
Indicates an estimated or not precisely known count of rooms associated with an entity.
-
B.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
C.
guestCountApproximate
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
D.
floorCountApproximate
Indicates an approximate or estimated number of floors associated with a building or structure.
-
E.
hasApproximateNumberOfResponsa
Indicates that an entity is associated with a rough or estimated count of responsa, rather than an exact number.
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95151e7348190a1d4953a8b416a13 |
completed | April 10, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69d94d3c27a08190a0237200203e476d |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:56 p.m.