Triple
T27062600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Rocky Tract |
E685084
|
entity |
| Predicate | numberOfMonumentalTombs |
P173199
|
FINISHED |
| Object | more than 100 |
—
|
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 | Statement: [The Rocky Tract, numberOfMonumentalTombs, more than 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMonumentalTombs Context triple: [The Rocky Tract, numberOfMonumentalTombs, more than 100]
-
A.
hasNumberOfMonumentalTombs
chosen
Indicates the specific count of monumental tombs associated with an entity.
-
B.
numberOfPaintedTombs
Indicates the quantity of tombs that have been painted in relation to a given subject or context.
-
C.
hasTombs
Indicates that one entity possesses, contains, or is the location of one or more tombs associated with another entity.
-
D.
tombStructure
Indicates that one entity is a structure that serves as the tomb or burial place associated with another entity.
-
E.
numberOfImperialMausoleums
Indicates the count of imperial mausoleums associated with a given subject.
- 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_69ef14835fcc81908bd737b4267ae528 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69fe78e545888190a239af1a84280fa0 |
completed | May 8, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69fe7842742081908043eb950ed69f92 |
completed | May 8, 2026, 11:56 p.m. |
Created at: April 27, 2026, 8:22 a.m.