Triple
T20704217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanger Factory Outlet Centers, Inc. |
E508857
|
entity |
| Predicate | hasCorporateSector |
P20603
|
FINISHED |
| Object | real estate |
—
|
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: real estate | Statement: [Tanger Factory Outlet Centers, Inc., hasCorporateSector, real estate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCorporateSector Context triple: [Tanger Factory Outlet Centers, Inc., hasCorporateSector, real estate]
-
A.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
B.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
C.
hasCorporateGroup
Indicates that one corporate entity belongs to, or is associated with, a broader corporate group or conglomerate.
-
D.
hasCorporateOffice
Indicates that an entity maintains a formal corporate office at a specified location or within another organizational entity.
-
E.
hasIndustrialSector
chosen
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
- 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c18ea874819092a125d9f929a311 |
completed | April 21, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:13 p.m.