Triple
T6221557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terra Nova |
E139125
|
entity |
| Predicate | numberOfDrillingCenters |
P68983
|
FINISHED |
| Object | multiple subsea drilling centers |
—
|
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: multiple subsea drilling centers | Statement: [Terra Nova, numberOfDrillingCenters, multiple subsea drilling centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDrillingCenters Context triple: [Terra Nova, numberOfDrillingCenters, multiple subsea drilling centers]
-
A.
numberOfDistributionCenters
Indicates the quantity of distribution centers associated with a given entity.
-
B.
hasNumberOfCentres
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given entity.
-
C.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
D.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
E.
hasNumberOfClinics
Indicates the quantity of clinics associated with or belonging to a given entity.
- 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062bddb688190add53172a7445d01 |
completed | March 22, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69c055ffdf54819086d987d646e44ff5 |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:22 p.m.