Triple
T37186139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pantalica archaeological area |
E921325
|
entity |
| Predicate | numberOfTombsApprox |
P51860
|
FINISHED |
| Object | 5000 |
—
|
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: 5000 | Statement: [Pantalica archaeological area, numberOfTombsApprox, 5000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTombsApprox Context triple: [Pantalica archaeological area, numberOfTombsApprox, 5000]
-
A.
numberOfPaintedTombs
Indicates the quantity of tombs that have been painted in relation to a given subject or context.
-
B.
hasTombs
Indicates that one entity possesses, contains, or is the location of one or more tombs associated with another entity.
-
C.
numberOfGravesApproximate
Indicates that the stated count of graves is an estimated or approximate number rather than an exact figure.
-
D.
hasApproximateNumberOfTombs
chosen
Indicates that an entity is associated with a tomb count that is approximate rather than exact.
-
E.
hasNumberOfMonumentalTombs
Indicates the specific count of monumental tombs associated with an entity.
- 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_69f76ea250bc819083f28d81de25cd0c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.