Triple
T23356841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tel Megiddo National Park |
E593072
|
entity |
| Predicate | hasStrataCountApprox |
P151996
|
FINISHED |
| Object | over 20 occupation layers |
—
|
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: over 20 occupation layers | Statement: [Tel Megiddo National Park, hasStrataCountApprox, over 20 occupation layers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrataCountApprox Context triple: [Tel Megiddo National Park, hasStrataCountApprox, over 20 occupation layers]
-
A.
strataAre
Indicates that one or more entities are classified as geological or structural layers (strata) in relation to something else.
-
B.
hasNumberOfFlats
Indicates the relationship that specifies how many flats (individual dwelling units) are associated with a given entity.
-
C.
sectorCountApprox
Indicates that the number of sectors involved is an approximate or estimated count rather than an exact value.
-
D.
hasNumberOfStreets
Indicates the relationship that specifies how many streets are associated with or contained within a given entity.
-
E.
hasApproximateIslandsCount
Indicates that an entity is associated with an estimated or non-exact number of islands.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a18996c81909c7ad15cde616553 |
completed | April 29, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69effcfd8d288190937a887fe6023c11 |
completed | April 28, 2026, 12:19 a.m. |
| PDg | Predicate description generation | batch_69f01d88b4ec8190a2a17a88e0eda178 |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 17, 2026, 5:28 p.m.