Triple
T105122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reading Terminal Market |
E2121
|
entity |
| Predicate | hasApproximateVendorCount |
P5785
|
FINISHED |
| Object | over 70 vendors |
—
|
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 70 vendors | Statement: [Reading Terminal Market, hasApproximateVendorCount, over 70 vendors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateVendorCount Context triple: [Reading Terminal Market, hasApproximateVendorCount, over 70 vendors]
-
A.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
B.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
C.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given context.
-
D.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
E.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563be81c81908ccc5ed44edd6b8e |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2570f45bc81909ebba7ee5f602976 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.